Jobs Data Engineer

Work Schedule: Full-time, US business hours

This is a hands-on data engineering role in Google Cloud. BigQuery and GCP services are your daily tools, not something you've touched once. This is not a backend or full-stack development role.

About the role
We're hiring a GCP Data Engineer for a US-based client. You'll own data pipelines, integrations and LLM-powered workflows across multiple client GCP projects at the same time, working independently from design to deployment.

What you'll do
- Build and maintain BigQuery pipelines: scheduled queries, data transfers and incremental loads
- Deploy and run services on Cloud Run, Cloud Functions and Cloud Scheduler
- Manage IAM, Secret Manager and Cloud Monitoring across many GCP projects at once
- Integrate external systems via REST APIs, webhooks and SFTP
- Build LLM-powered workflows using OpenAI, DeepSeek and Perplexity APIs, including prompt design
- Ship code with Git and Docker

Must-have
- 2+ years of commercial data engineering experience on GCP (experience on AWS or Azure only does not count)
- Advanced BigQuery SQL in production: scheduled queries, data transfers, incremental loads
- Hands-on with Cloud Run, Cloud Functions, Cloud Scheduler, Secret Manager, IAM and Monitoring
- Strong Python
- REST APIs, webhooks and SFTP integrations
- Git and Docker
- LLM APIs (OpenAI, DeepSeek or Perplexity) used in production, with prompt writing
- Fluent English: you'll communicate directly with the US client every day
- Full working overlap with PST (US Pacific time)
- A portfolio or examples of your work: GitHub, case studies or detailed project descriptions

Nice to have
- Terraform
- n8n
- Supabase / Postgres
- Clay
- Google Meridian
- Lead scoring or marketing-tech experience
- Experience reducing GCP and LLM costs

Please don't apply if
- Your main experience is backend or full-stack development
- Your cloud experience is mainly AWS or Azure
- You can't work PST business hours
- You can't name the companies you've worked for

What we offer
- Long-term engagement with a US client
- Fully remote
 

How to apply
Start your application with the word "BigQuery" and add 2–3 sentences about the most complex GCP pipeline you've built. Applications without this won't be reviewed.

Laba Group is one of the largest international EdTech companies in Eastern Europe. Currently, our team consists of over 800 colleagues united by a single mission: to improve people’s lives through relevant and systematic knowledge.
In 10 years, we have launched over 1,400 courses and educated more than 200,000 students globally. We have also created 5 online education brands:

• Laba — an online business school
• Skvot — a school of popular culture
• robot_dreams — a platform for growth in IT
• Vocabulaba — a school of English for professionals
• elvtr — an online school in the US and the UK

We are seeking a JuniorData Engineer to join our team. You will build and maintain reliable data pipelines, integrate diverse data sources, and create reporting datasets under the guidance of experienced team members. You will focus on ensuring high data quality and delivering business-oriented solutions aligned with company goals.

What will be the main responsibilities?

• Design, build, and maintain scalable ETL/ELT pipelines.
• Integrate data from internal and external systems (CRMs, LMS, marketing platforms, databases, etc.).
• Develop and maintain API integrations (REST/JSON, OAuth, etc.).
• Automate recurring data processes using Python (or similar scripting languages).
• Design and maintain data models optimized for analytics and operational reporting.
• Work with cloud data warehouses (e.g., BigQuery, Snowflake) to ensure efficient storage and retrieval.
• Implement data validation and monitoring mechanisms to ensure accuracy and reliability.
• Proactively monitor and resolve data pipeline failures.
• Help build dashboards and data applications using BI tools, low-code/no-code platforms, and AI coding assistants.
• Translate complex datasets into clear, actionable insights for business stakeholders.
• Collaborate with analysts, data scientists, and business teams to define KPIs and reporting requirements.
• Suggest improvements to existing data flows and business processes.
• Maintain structured and comprehensive technical documentation (pipelines, integrations, data definitions, architecture).
• Participate in cross-functional discussions to align technical implementation with business needs.
   

Requirements

• Practical Python skills  (or similar scripting language)  for data processing, API requests, and automating recurring tasks.
• Working knowledge of SQL, including joins, aggregations, CTEs, and window functions.
• Basic understanding of REST APIs, JSON, authentication methods, and pagination.
• Experience working with cloud platforms (GCP, AWS).
• Understanding of ETL/ELT concepts and relational database fundamentals, including tables, keys, and relationships.
• Familiarity with Git for version control and collaboration.
• Experience using LLMs and AI coding tools to build, debug, and automate solutions, including reviewing and validating AI-generated code.
• Ability to investigate data issues, validate results, and document your work.
• Around 1 year of experience in a data engineering or data-focused role.
  
  Skills that would be “nice to have”:
• Experience with BI, low-code/no-code, or workflow automation tools, such as Power BI, Retool, or Google Sheets.
• Experience optimizing query performance and cost efficiency in cloud environments.
• Experience working with CRM, LMS, or marketing platform data.
• Basic understanding of CI/CD and automated data quality checks.

BenefitsWhy Laba Group is the Place to Be 🔥:
 

• Flexible full-time. We focus on results, not hours worked: manage your own time, stay connected with the team, and meet deadlines.
• Remote, onsite, or hybrid — it’s up to you! Our modern workspace in Kyiv is fully equipped with everything you need.

As part of our team, you’ll get:
 

• Access to all Laba Group courses for you and your family.
• Compensation for external training, sports, and mental and physical health, helping you stay balanced and prioritize your well-being.
• Unlimited vacation after 6 months of working with us. Plan according to your needs and adjust based on workload intensity because rest is essential for your health.
• We create a supportive environment where facing challenges and making mistakes isn’t scary. A place to find like-minded people and become part of a larger community. A service that truly puts the client first.
• No micromanagement or bureaucracy — only automated processes. Feel free to experiment, propose, and implement your ideas.
• We champion a proactive mindset, bold ideas, and a willingness to embrace change. Laba isn’t about going with the flow — it’s about pushing the world forward.
• We provide only up-to-date insights from market experts—knowledge that can be immediately applied in practice, transforming workflows even during the learning process. Our products are designed for decisive action in business and careers.
• A socially responsible environment: we regularly launch charity projects and fundraisers to support Ukraine and its defense forces.
• Vertical and horizontal growth: 83% of our Heads of Departments and Team Leaders have been promoted internally.

Our recruitment process is transparent and straightforward.
Rectuiter → an interview with the Data Engineer Lead → Offer

We’re not looking for perfect candidates. We’re looking for bold and “one of us” people. 💛
Join the team. It’s going to be an amazing journey.

SouthRivers Data is an EU-based data and AI engineering company headquartered in Bratislava, Slovakia. We design and deliver data platforms and production AI systems for companies in the US, UK and Europe, and run several client projects in parallel.

Our client, a US-based B2B company, is building an AI-powered analytics platform on top of its sales and operational data. The platform ingests data from the CRM and other business systems, models it in PostgreSQL and calculates business metrics in a dedicated analytics layer. Teams then explore the results through a natural-language interface. The LLM works only on validated data and calculated results, so every number it reports can be traced back to the source.
You join as the senior engineer responsible for the data and AI layers of the platform.

Your responsibilities: 

• Build ingestion pipelines and API integrations with the CRM and other business systems
• Design the PostgreSQL data model and the metrics layer
• Build the LLM layer: natural-language querying through tool calling, and classification of call transcripts and other unstructured data
• Implement RAG over company documents with hybrid search and re-ranking
• Deploy and run the platform in the cloud with tests, monitoring and cost tracking
• Present architecture decisions and demos to the client

What we expect:

• 5+ years in data or software engineering, including 2+ years building LLM features in production
• Strong Python (FastAPI, pandas/polars, pytest) and advanced SQL
• PostgreSQL data modelling and migrations on live data; experience with BigQuery or Snowflake
• Pipeline orchestration with Airflow, Prefect or dbt, including incremental loads, backfills and data-quality checks
• API integrations: authentication, pagination, rate limits, retries, changing source schemas
• LLM APIs (OpenAI, Anthropic, Gemini): structured outputs, tool calling, cost tracking, evaluation of answer quality
• At least one RAG system shipped to real users: chunking, embeddings, pgvector or a vector database, hybrid search, re-ranking
• Deployment on GCP or AWS with Docker, CI/CD and proper secrets management
• Daily work with Claude Code or a similar AI coding agent, and the judgement to review its output critically
• English at Upper-Intermediate or higher; you will lead client calls and demos

Nice to have:

• TypeScript/React or Next.js for internal tools
• LangGraph, Claude Agent SDK, Langfuse or LangSmith
• Kafka or other streaming systems
• Knowledge graphs, entity resolution, embedding-based clustering
• Experience with marketing, sales or CRM data
• Direct work with US clients

What we offer:

• Work on production AI systems for clients in the US, UK and EU
• Continuous engagement across projects, with no gaps between assignments
• Fully remote with a flexible schedule
• Possible part-time collaboration
• Architecture reviews and technical guidance from a Data Architect
• Real ownership: you make architecture decisions and present them to the client
• Long-term collaboration

Beyond this project:
We run multiple data and AI projects at the same time: RAG systems, LLM processing pipelines, data platform modernization, and technical audits of AI platforms. Depending on project priorities, you may move to another project. Any move will use a similar stack, match your seniority, and come with advance notice. When one engagement ends, the next one is already in place.

Job Title:

Senior Data Engineer

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

The product brings together data about employees, devices, applications, and IT operations to help organizations understand and improve the digital workplace experience. It combines information from multiple systems into a consolidated view, enabling IT teams to identify issues, analyze trends, and take action.

Its underlying platform ingests continuously changing data, normalizes it, and maintains relationships between employees, departments, devices, and organizational structures. Role-based access controls govern how that information is used. The engineering challenge is to make high-volume data flows reliable and efficient while providing low-latency access to current information.

About the Role:

As a Senior Data Engineer, you will build the streaming pipelines and distributed services that support this platform. This is a hands-on role centered on Java, Kafka, and Apache Flink. You will work from product requirements and architectural direction while owning complex implementation decisions, identifying risks early, and delivering production-ready components with minimal supervision.

You will join a small team developing the product and work with architecture, product, and DevOps colleagues. Early work includes understanding existing schemas and data sources, planning how data enters the platform, defining transformations and aggregations, and implementing, testing, and deploying those flows. The role involves substantial work on throughput, latency, persistence, reliability, and the operational behavior of systems processing data at scale.

Key Responsibilities:

• Build and maintain production Java services and high-throughput data-processing components.
• Design, implement, and optimize Kafka and Apache Flink pipelines that combine data from multiple sources.
• Plan and implement data flows, including ingestion, normalization, calculation, aggregation, and persistence.
• Develop the data model and services for employees, departments, devices, organizational relationships, and role-based access control.
• Build service interfaces and data contracts using technologies such as gRPC and Protobuf.
• Design persistence and data-access patterns across relational, analytical, and in-memory systems, including PostgreSQL, ClickHouse, and Redis where appropriate.
• Optimize schemas, queries, indexes, partitioning, and ingestion for large datasets.
• Diagnose bottlenecks and production incidents across streaming pipelines, services, and databases.
• Improve scalability, reliability, observability, throughput, latency, and operational cost.
• Use AI-assisted development tools while independently reviewing their output for correctness, security, and maintainability.

Required Competence and Skills:

• Typically 10+ years of experience building and maintaining production systems, including substantial hands-on work with distributed, high-throughput data platforms.
• Typically 5+ years of professional Java experience, with a strong grasp of concurrency, memory management, and JVM performance tuning.
• Typically 3+ years of production Kafka experience, including partitioning, consumer groups, schemas, delivery guarantees, fault tolerance, and throughput tuning.
• Typically 2+ years of production Apache Flink experience, including stateful processing, windowing, checkpointing, backpressure, fault recovery, and pipeline optimization.
• Experience building and operating systems that process millions of events per minute, with direct ownership of scaling or production performance decisions.
• Strong data-modeling and database optimization skills, including work with time-series data, high-volume ingestion, analytical workloads, and PostgreSQL or another relational database.
• Experience with Protobuf, schema evolution, and compatibility in distributed systems; familiarity with gRPC and service-to-service communication.
• Hands-on experience monitoring, troubleshooting, and improving distributed production workloads, including incident resolution and resource management.
• Practical use of AI-assisted or agentic coding tools, paired with critical review of generated code before production use.
• Ability to own complex components independently, assess technical tradeoffs, and deliver maintainable solutions.

Nice to Have:

• Experience with ClickHouse or a comparable large-scale analytical database.
• Practical familiarity with CI/CD, Docker, Kubernetes, and production observability.
• Experience with TypeScript and React, including backend services or user-facing applications.
• Experience unifying data from multiple sources in Kafka-based pipelines.
• Experience with real-time analytics, anomaly detection, or complex event processing.
• Experience integrating AI or LLM-based capabilities, agentic workflows, or tool-based automation into production systems.

Why Us:

We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

We provide full accounting and legal support in all countries we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

We are seeking a Senior Data Engineer for an initial 6-month project, with the possibility of extension.

Schedule & Team Integration: 4-5 hours of overlap with US Central time (CT) (till 9-10 PM Kyiv time)

Together with the Zoolatech team, our partner, a leading construction management company, is at the forefront of developing a cloud-based software solution. The platform is instrumental in the successful completion of major projects including skyscrapers, hospitals, data centers, stadiums, and schools. It streamlines the management of finances, quality, teams, and risks, making these processes transparent, convenient, and regulation-compliant.

Our partner is not only an expert in construction management but also a significant technology innovator, actively developing web and mobile solutions. Zoolatech plays a crucial role in supporting their growth, and we are looking for result-oriented engineers who can maintain high development standards.

You’ll join the data engineering team behind the platform’s analytics and customer-facing insights. The team ingests data from multiple operational sources into a lakehouse architecture and turns it into trusted, well-governed datasets for data scientists, analysts and product teams.

Responsibilities: 

• Designing and maintaining scalable ingestion and transformation pipelines on Databricks, with jobs defined as code and deployed through CI/CD.
• Owning end-to-end pipelines, including usage and business metrics pipelines and large-scale historical backfills across several data domains.
• Consolidating and standardizing pipeline and job definitions into shared, reusable frameworks.
• Supporting customer-health and usage analytics with reliable, well-modelled data.
• Managing access, permissions, and monitoring so that data is secure and issues are caught early.
• Working with data science, platform and product teams to turn requirements into production-grade data products.

Requirements:

• 6+ years of hands-on data engineering experience.
• Strong Python and SQL.
• Databricks (Spark, Delta).
• Airflow or a similar orchestration tool.
• Hands-on experience with a major cloud platform, ideally AWS (e.g. S3, IAM, networking basics, cost and access management).
• Strong software engineering practices: GitHub, CI/CD, and code reviews.

• Hands-on production experience in data modeling, backfills, and data quality, beyond building basic pipelines.

   

• Nice to have
  • Snowflake.
  • AWS.
  • Postgres/Lakebase.
  • Daily use of AI coding tools (e.g. Claude, Cursor).

We are looking for a Software Engineer who will take ownership of the technical link between the core data platform and the semantic layer at our client — an international AdTech company headquartered in New York. The company builds analytics and verification software for digital advertising and evaluates every ad impression against four criteria: fraud prevention, brand safety, viewability and geo-location. The role’s mission is to make sure data reaches UIs, CLI tools and AI agents reliably and on time.
 

Responsibilities:

• Developing scalable solutions that extend application capabilities and improve data processing efficiency and system performance;
• Leading the technical implementation between the core data platform and the semantic layer, ensuring reliable data delivery to downstream applications and workflows;
• Designing data endpoints in the semantic layer that turn raw source data into structures usable by UIs, CLI tools and AI agents;
• Establishing monitoring, data quality checks and operational best practices across pipelines for faster issue detection and resolution;
• Taking full technical ownership of complex initiatives spanning data infrastructure and application/API components;
• Working with product managers and the Data, Applications, Data Science and Customer Success teams to clarify requirements and ensure accurate, timely data delivery.
   

Requirements:

• 7+ years of software development experience, including building and supporting production systems with a focus on reliability, scalability and performance;
• Solid expertise in contract-based architecture, API design and building semantic and serving layers on top of data lakes or warehouses;
• Hands-on experience with data lake / warehouse technologies: Parquet, DuckDB, S3-compatible object storage, lakehouse table formats (Iceberg), cloud warehouses (Snowflake);
• Strong skills in at least one general-purpose language (Python or Go) and advanced SQL, with the ability to choose technologies based on project needs;
• Experience with orchestration, containerization and Kubernetes;
• Solid background in monitoring, observability and data quality practices;
• Confident use of AI development tools (coding assistants, LLMs, agents) and interest in secure interfaces and contracts for AI-driven data access;
• Ability to explain complex technical topics to non-technical stakeholders;
• Proactive, self-driven and results-oriented mindset;
• Availability for occasional meetings at 7–9 PM Kyiv time;
• English at C1 level.
   

Nice to have:

• Experience with observability platforms (Prometheus, Grafana, OpenTelemetry) and data quality frameworks;
• Knowledge of marketing analytics: multi-touch attribution (MTA), marketing mix modeling (MMM), experimentation or customer data platforms.
   

What we offer:

• Remote work with no location restrictions.
• Flexible working schedule.
• Paid vacation.
• Paid sick leave.
• Courses and professional training.
  
  Send your CV!

TechBar is a software services company working with clients across the US and Europe. We are investor-backed, growing steadily, and known for putting senior, thoughtful engineers in front of our clients.

We are looking for a Senior Data Engineer to join our client’s team at a wealth management firm modernizing its data platform using Databricks and AWS.
 

About the Project

You will design, build, and maintain reliable data pipelines and lakehouse models that power analytics, reporting, and data products across the business.

The platform processes financial data, including client portfolios, transactions, positions, and performance. Some initiatives may involve migrating existing workloads from Snowflake to Databricks.
 

Responsibilities

• Build the lakehouse: Design and develop scalable batch and streaming pipelines on Databricks using Spark and Delta Lake, following a medallion architecture (Bronze / Silver / Gold).
• Own data on AWS: Work with S3, IAM, and related AWS services to ingest, store, and secure data from internal systems and third-party financial data providers.
• Model for the business: Build clean, scalable, and well-documented data models for analytics, reporting, and downstream consumers.
• Ensure data quality and trust: Implement data quality checks, monitoring, and lineage. Manage access and governance using Unity Catalog.
• Optimize performance and cost: Tune Spark jobs, cluster configurations, and storage layouts to improve performance and control cloud costs.
• Automate delivery: Apply CI/CD and infrastructure-as-code practices to Databricks workflows and environments.
• Support migration: Assess and migrate data pipelines, models, and workloads from Snowflake to Databricks where needed.
• Collaborate: Work closely with analysts, data scientists, and business stakeholders to translate requirements into reliable data solutions.
   

Requirements

• 6+ years of experience in data engineering, including significant hands-on experience with Databricks in production.
• Strong knowledge of Apache Spark / PySpark and advanced SQL.
• Solid Python skills for data engineering.
• Hands-on experience with AWS as the underlying cloud platform for data systems, including S3, IAM, and related data services.
• Experience with Delta Lake and lakehouse / medallion architecture.
• Experience building data models for analytics and reporting.
• Experience with CI/CD, Git, and production data operations, including monitoring, troubleshooting, and performance tuning.
• Ability to own data pipelines end-to-end, from design and implementation to production support.
• Strong understanding of data quality, security, governance, and cost optimization.
• Excellent communication skills, with the ability to collaborate with both technical and non-technical stakeholders.
• Ability to work independently and proactively in a distributed team.
• Fluent English.
• Currently based in the United States or the United Kingdom.

Tech Stack
 

Data Platform

• Databricks
• Apache Spark / PySpark
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Delta Live Tables
   

Cloud Infrastructure

• AWS S3
• IAM
• AWS Glue
• AWS Lambda
• Kinesis / MSK
• CloudWatch
   

Languages

• Python
• SQL
• Scala (a plus)
   

Engineering Practices and Tools

• Git
• CI/CD
• Terraform or Databricks Asset Bundles
• Data quality frameworks
• Orchestration with Databricks Workflows or Airflow
   

Adjacent Technologies

• Snowflake (migration context)
• dbt
• BI tools used by downstream consumers
   

Nice to Have

• Experience migrating from Snowflake to Databricks or working on other large-scale data platform migrations.
• Experience with Unity Catalog governance and access management.
• Experience building streaming pipelines using Structured Streaming, Kafka, or Kinesis.
• Experience with Terraform or Databricks Asset Bundles.
• Experience with dbt.
• Databricks certifications: Data Engineer Associate or Professional.
• Experience in wealth management, asset management, or broader financial services, particularly with portfolios, positions, transactions, market data, and reference data.
• Experience working with regulated or sensitive data, including PII, compliance, and audit requirements.
   

What We Offer

• 20 paid vacation days per year.
• 10 paid sick days per year.
• 6 additional special days off per year.
• Company-provided equipment to ensure you have everything you need to work effectively.
• The opportunity to contribute to a modern Databricks and AWS data platform in the financial services domain.
• A long-term opportunity within an international, distributed team.
   

Location

We are considering candidates based in the United States and the United Kingdom only.

If you are a Senior Data Engineer with strong Databricks, Spark / PySpark, Python, SQL, and AWS experience who enjoys taking ownership of data solutions end-to-end, we look forward to hearing from you!

The project is for one of the world's famous science and technology companies in the pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future. Modernizing the data infrastructure through the transition to Snowflake as a priority, as it will enhance capabilities for implementing advanced AI solutions and unlock numerous opportunities for innovation and growth.

We are seeking a hands-on senior engineer to help design, build, and operate cloud data products and services. The role combines AWS cloud engineering, data engineering, analytics, and production delivery in an enterprise environment.

• Responsibilities:

  - Design and deliver AWS solutions using services such as S3, Glue, Lambda, ECS, IAM, and MWAA/Airflow.
  - Build reusable Terraform modules and maintain cloud infrastructure through established CI/CD pipelines.
  - Develop data ingestion, transformation, and analytics workflows using Glue, Snowflake, and dbt.
  - Implement data quality checks, monitoring, and processes for investigating and resolving issues.
  - Deliver products and services through the full software development lifecycle, including design, development, QA and testing, deployment across development, test, and production environments, and operational support.
  - Apply responsible agentic engineering when building or using AI-enabled tools and workflows, with appropriate evaluation, human oversight, security, and data protection.
  - Work with product and business teams to turn requirements into secure, maintainable solutions.

• Mandatory Skills Description:

  - Strong AWS cloud expertise;
  - Production experience with Terraform, Python, SQL, and data engineering and analytics patterns;
  - practical understanding of security, CI/CD, testing, and responsible use of AI agents.

• Nice-to-Have Skills Description:

  Experience with Snowflake, dbt, Azure DevOps, and regulated enterprise environments

Are you passionate about building scalable, high-performance data solutions? We are looking for a Data Engineer to join our team and help design and develop robust data platforms that power analytics, reporting, and data-driven decision-making.

Why join us? You will work on challenging projects, collaborate with talented experts, and have access to professional growth opportunities, a supportive environment, and competitive benefits.

Responsibilities:

• Design and develop scalable, reliable data platforms and pipelines for analytics and operations

• Implement data processing workflows using distributed frameworks (Apache Spark/pySpark, Databricks)

• Evolve the organization’s cloud data platform leveraging Azure technologies (Data Factory, Synapse, Event Hub, Azure Data Lake Storage)

• Model data to support analytics, reporting, and downstream consumption

• Integrate and process data from multiple sources ensuring quality and consistency

• Monitor and optimize pipeline performance, ensuring reliability and scalability

• Collaborate with analysts, scientists, and engineers to translate requirements into solutions

• Improve data engineering standards, testing, and operational reliability

• Evaluate and introduce new tools and technologies in the data engineering ecosystem

Requirements:

• 4+ years of hands-on experience in data engineering or building data processing platforms

• Strong SQL skills and solid Python experience for pipeline development

• Experience with distributed processing frameworks (Apache Spark/pySpark, Databricks preferred)

• Practical experience designing and implementing pipelines in cloud environments (Azure preferred)

• Experience with production-scale analytical systems and data modeling

• Understanding of ETL/ELT design, dimensional modeling, and data warehousing principles

• Experience with modern data lake architectures and formats (Parquet, JSON)

• Familiarity with workflow orchestration tools (Airflow)

• Experience with database development and optimization

• Ability to design scalable solutions with minimal supervision

• Strong collaboration and communication skills

• Professional proficiency in English

______________________________________________________________________

Чи захоплюєтеся ви створенням масштабованих високопродуктивних рішень для роботи з даними? Ми шукаємо Data Engineer, який приєднається до нашої команди та допоможе проєктувати й розробляти надійні платформи даних для аналітики, звітності та ухвалення рішень на основі даних.

Чому варто приєднатися до нас? Ви працюватимете над складними проєктами, співпрацюватимете з талановитими фахівцями та матимете доступ до можливостей професійного розвитку, підтримувального робочого середовища й конкурентного пакета переваг.

Обов’язки:

• Проєктувати та розробляти масштабовані й надійні платформи даних і конвеєри даних для аналітики та операційної діяльності.

• Реалізовувати процеси обробки даних за допомогою розподілених фреймворків (Apache Spark/PySpark, Databricks).

• Розвивати хмарну платформу даних організації з використанням технологій Azure (Data Factory, Synapse, Event Hub, Azure Data Lake Storage).

• Моделювати дані для підтримки аналітики, звітності та подальшого використання даних.

• Інтегрувати й обробляти дані з різних джерел, забезпечуючи їхню якість і узгодженість.

• Відстежувати та оптимізувати продуктивність конвеєрів даних, забезпечуючи їхню надійність і масштабованість.

• Співпрацювати з аналітиками, науковцями та інженерами, перетворюючи вимоги на технічні рішення.

• Удосконалювати стандарти інженерії даних, підходи до тестування та операційну надійність.

• Оцінювати й упроваджувати нові інструменти та технології в екосистемі інженерії даних.

Вимоги:

• Понад 4 роки практичного досвіду в інженерії даних або розробці платформ для обробки даних.

• Впевнене володіння SQL і ґрунтовний досвід роботи з Python для розробки конвеєрів даних.

• Досвід роботи з фреймворками для розподіленої обробки даних (Apache Spark/PySpark, бажано Databricks).

• Практичний досвід проєктування та реалізації конвеєрів даних у хмарних середовищах (бажано Azure).

• Досвід роботи з аналітичними системами промислового масштабу та моделюванням даних.

• Розуміння принципів проєктування ETL/ELT-процесів, багатовимірного моделювання та побудови сховищ даних.

• Досвід роботи із сучасними архітектурами озер даних і форматами даних (Parquet, JSON).

• Знайомство з інструментами оркестрації робочих процесів (Airflow).

• Досвід розробки та оптимізації баз даних.

• Здатність проєктувати масштабовані рішення з мінімальним наглядом.

• Розвинені навички співпраці та комунікації.

• Професійний рівень володіння англійською мовою.

We are the creators of a new fintech era!

Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future.

We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies.

Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA.

Join us as a Head of Data Platform. 

Requirements:
Education & Experience:

— MSc in computer science, mathematics, statistics, physics or another quantitative field; PhD is a plus, not a requirement — research-grade methodology sits with the Quantitative Researcher

— 5+ years in data engineering / analytics, incl. lead / management experience; built a trading / financial data platform from zero at a crypto exchange, HFT / electronic-trading firm, market maker, or trading fintech. Bank / enterprise-DWH background alone does not qualify

— Personally carried production ownership of money data — trades, balances, positions, P&L — where a data error is a financial error

Core Technical Skills — data platform:

— Hands-on today: designs and ships pipelines personally — advanced SQL and Python; ClickHouse (or kdb+ / comparable columnar OLAP) in production

— Streaming and batch in one architecture: Kafka or equivalent, event feeds with second-grade freshness alongside hourly aggregates; trading-day logic — metrics counted within a trading day that starts and ends at a fixed morning hour, with an automatic daily reset

— Optimizes data footprint and computational overhead across every stage: selects the tech stack, partitioning logic, and execution flow based on cost-to-performance impact

— Data-correctness engineering: reconciliation, idempotent backfills, late / duplicate event handling, data-quality monitoring and lag alerting

— Vendor and API integration: institutional market-data providers (Kaiko / Amberdata class), exchange APIs, FX feeds; schema design and documentation others can build on

Core Technical Skills — analytics & applied math:

— Applied statistics at a working level: distributions, hypothesis tests and confidence intervals, correlation vs causation, time-series and aggregation pitfalls (session boundaries, survivorship, outliers) — enough to catch a methodological error in an analyst's query or notebook, and to have designed or reviewed metric / experiment methodology in past roles

— Metric and semantic design: one written definition per metric (P&L, uptime, spread, fill rate) enforced across every report; analytical data models (marts / star schemas) that make those definitions computable and cheap to query

— BI stack governance: Tableau / Grafana — performance, access control, versioned metric definitions; reviews dashboards for correctness rather than building them all himself

— ML literacy: understands when a model is — and is not — needed and can supervise applied work; research-grade modeling remains the Quantitative Researcher's mandate

Market & Domain Knowledge:

— Trading data from practice, not textbooks: maker/taker, fees and rebates, perpetual funding, realized / unrealized P&L, inventory and exposure, order-book depth and spread, slippage, quoting uptime

— Understands the consumers: how analysts, quants and desk heads use the data, and how market-quality / compliance metrics feed liquidity-provider terms

— Has built and run a mixed organisation of data engineers and analysts: separate hiring bars and growth tracks, peer review of queries and dashboards as a standard, methodology documented before a number ships

— Partners with quantitative research: turns the Researcher's methodology into production metrics and dashboards without distorting it; runs the sign-off process with quant and desk stakeholders

— Treats balances and client data as confidential by default: access control, least privilege, audit trail, no raw-data leaks

— Never ships silently wrong numbers: escalates early, cuts scope consciously, flags uncertainty in the data

Mindset & Soft Skills:

— Player-coach: the first months are personal hands-on delivery; scaling through the team runs in parallel, not instead

— Speaks business: reports status in money and deadlines; prioritizes by P&L impact — which dataset unblocks which decision; no jargon walls with management and stakeholders

— Owner of the direction's technical goals, not of a backlog: takes a business problem, decomposes it into tasks, executes and delegates

Responsibilities — Platform:

— Architect and ship the central data warehouse: every trade, position, balance and P&L across all trading services; an hourly contour for BI plus a real-time contour for monitoring; trading-day logic with automatic daily reset

— Build the data layers behind LP terms: market quality (spread, depth near mid price, fill rate, per-order slippage), quoting-uptime start/stop events per maker and pair, external tier-1 benchmarks and FX feeds for regional fiat pairs

— Close the data debt: gaps in realized-P&L coverage, history backfills, broken monitoring services — restore or replace

— Set and hold agreed data-freshness targets and pipeline-health alerting; own schemas and documentation

— Own analytics delivery end-to-end: the KPI rulebook published and signed, financial / attribution / historical-performance dashboards, compliance screens and provider scorecards

— Enforce a single semantic layer: one definition per metric across every report; methodology documented before a dashboard ships

— Quality gate: numbers are reviewed and challenged before they reach management; sign-offs from quant / desk collected per deliverable

Responsibilities:
— Hire leaders-first and onboard the technical team (~10 people: data engineering, analytics & BI, technical PM) per the approved hiring plan; hand tasks down without losing velocity

— Single interface to exchange tech (requirements, acceptance, freshness targets) and to analytics / quant stakeholders; run delivery in Jira against the approved one-year roadmap

— After the build: run the platform as a service to the direction — researcher-generated tasks, new metrics and pair groups, regular recalibration. 

Working terms

Immerse yourself in Crypto & Web3:

• Master cutting-edge technologies and become an expert in the most innovative industry.

Work with the Fintech of the Future:

• Develop your skills in digital finance and shape the global market.

Take Your Professionalism to the Next Level:

• Gain unique experience and be part of global transformations.

Drive Innovations:

• Influence the industry and contribute to groundbreaking solutions.

Join a Strong Team:

• Collaborate with top experts worldwide and grow alongside the best.

Work-Life Balance & Well-being:

• Modern equipment.
• Comfortable working conditions, and an inspiring environment to help you thrive.
• 22 business days of paid leave.
• Additional days off for national holidays.

We’re looking for a Junior Data Engineer with 1–2 years of experience to help build, maintain, and improve data pipelines and data processing solutions.

You’ll work closely with experienced engineers and other team members to develop reliable data workflows, improve data quality, and grow your expertise in data engineering.

What you’ll do
- Build and maintain ETL/ELT pipelines from databases, APIs, and other data sources
- Write and optimize SQL queries for data processing and transformation
- Develop data processing scripts using Python
- Assist with data modeling and maintaining data warehouse structures
- Work with Airflow or similar workflow orchestration tools
- Implement data validation and quality checks
- Monitor pipeline execution and help troubleshoot issues
- Participate in improving pipeline performance and reliability
- Use Git for version control and collaborate through code reviews
- Document data pipelines and technical processes

What we’re looking for
- 1–2 years of experience in Data Engineering or a related technical role
- Good knowledge of SQL and basic to intermediate Python skills
- Understanding of ETL/ELT processes and data transformation concepts
- Familiarity with relational databases and data warehousing fundamentals
- Basic experience with Airflow or similar orchestration tools, or willingness to learn
- Familiarity with at least one cloud platform: AWS, Azure, or GCP
- Understanding of Git, testing, and data quality principles
- Analytical thinking, attention to detail, and a willingness to learn
- Intermediate English or higher

Nice to have
- Experience with Spark / PySpark
- Familiarity with Databricks, Delta Lake, or lakehouse architecture
- Basic knowledge of Kafka, CDC, or streaming data
- Familiarity with Terraform or other Infrastructure as Code tools
- Experience with CI/CD pipelines

🎁 What we offer
- 100% remote work
- Flexible working hours
- Competitive compensation
- Paid vacation and sick leave
- Health insurance / medical support
- Professional development and learning opportunities
- A friendly, supportive team with direct communication
- The opportunity to work on international projects and grow your data engineering skills

In our team, we have few Backend engineers and one ML engineer. We are hiring a Senior Data Engineer to build and own our analytics platform from the ground up.

Current Technology Stack

• Backend & data: Java/Spring, MySQL, MongoDB, Redis; tens of millions of behavioral/session records.
• Analytics & ML: Metabase (currently querying operational databases), Python, Polars, Parquet, CatBoost, FastAPI.
• Infrastructure: Docker, Kubernetes; DigitalOcean → AWS EKS migration; Terraform, Helm, Argo CD, ECR, Secrets Manager, Cloudflare.
• Data platform: No dedicated warehouse or orchestrator yet; technology selection is part of this role.

Some of our sites:
vinclarity.com - vehicle history reports

thathome.com - property records
efaq.com - psychological, educational, and just-for-fun quizzes

Key Responsibilities

• Design and implement a cost-effective analytical warehouse, ingestion pipelines, transformation models and orchestration.
• Integrate MySQL/MongoDB, payments, fraud, email/helpdesk, advertising APIs and quiz/session events; support CDC or incremental loads, retries and safe backfills.
• Own reliable models for subscriptions, revenue, cohorts, LTV, CAC and ROAS; improve Metabase performance and reduce analytical load on production.
• Implement data quality checks, monitoring, reconciliation, documentation, data contracts and access controls.
• Partner with the ML Engineer on reproducible, point-in-time correct training datasets; prevent data leakage and handle delayed labels.

Requirements

• 5+ years in Data Engineering; demonstrated ownership of production pipelines or a data platform.
• Advanced SQL and MySQL optimization; MongoDB/document data modeling; analytical and dimensional modeling.
• Strong Python with pandas or Polars, Parquet; ETL/ELT, incremental processing, idempotency, backfills and recovery.
• Production experience with a data warehouse and an orchestrator (e.g., Airflow, Dagster or Prefect); ability to choose appropriate tools.
• AWS fundamentals, Docker, Git/CI/CD; working familiarity with Kubernetes; pragmatic architectural judgment.
• English B2+.

Nice to have

• dbt; Redshift, ClickHouse, Snowflake or BigQuery; CDC/Debezium or Airbyte; subscription billing, marketing attribution and fraud data; Terraform/Helm/Argo CD; Java/Spring familiarity.

Conditions

• Fully remote setting
• Corporate devices (laptop, power station)
• Unlimited PTO (paid time off) policy
• Udemy courses

Job Description
8+ years of software development experience, including experience working on complex backend, distributed systems, data infrastructure, or data-intensive projects.
Proven experience designing and developing scalable backend platforms, distributed services, query engines, data-processing systems, or infrastructure components handling large volumes of data or high-throughput workloads.
Strong hands-on experience with Scala, Java, or another JVM-based language, with the ability to work effectively in Scala-based production codebases.
Hands-on experience with Apache Spark, preferably with Scala, for large-scale distributed data processing.
Experience building or working with distributed query engines, SQL engines, or data access/query platforms, such as Trino, Presto, Spark SQL, Hive, or similar technologies.
Hands-on experience with modern data lake and table formats, such as Apache Iceberg, Delta Lake, Apache Hudi, or similar technologies.
Strong understanding of data lake architecture, including table formats, partitioning, schema evolution, snapshots, versioning/time travel, data consistency, transactions, and compaction.
Experience working with large-scale production data platforms, including environments with large numbers of tables, partitions, or high-volume data processing workloads.
Deep understanding of distributed systems, data processing, storage engines, or data infrastructure, including scalability, fault tolerance, concurrency, and performance optimization.
Experience developing and troubleshooting high-throughput, low-latency, production-grade systems.

The role is primarily focused on backend, distributed systems, data infrastructure, and system-level engineering rather than full-stack application development.
Job Responsibilities
Architect & Build: Lead the architecture, design, and implementation of highly scalable, complex backend systems and robust data infrastructure using Scala.
Data Lake Engineering: Design and manage enterprise-grade data lake solutions leveraging Apache Iceberg or equivalent table formats, ensuring optimal data consistency, transactions, schema evolution, partitioning, and compaction.
Performance Optimization: Maximize system throughput, minimize latency, and optimize resource utilization across large-scale distributed data processing systems.
Production Excellence: Systematically diagnose, troubleshoot, and resolve complex, high-impact production and customer-facing infrastructure issues.
Codebase Modernization: Champion the gradual improvement and modernization of a mature, production-critical codebase without causing disruption to active customers or services.
Technical Leadership & Collaboration: Act as a technical authority, fostering strong communication and collaboration across engineering teams to align on technical objectives.
Navigate Ambiguity: Exercise a strong ownership mindset to operate effectively and deliver high-quality outcomes in areas with limited documentation or historical knowledge.
Department/Project Description
We are seeking an expert Data/Backend Infrastructure Engineer with strong distributed systems experience, large-scale data processing, cloud-native architecture, data lake/table formats, query engines, and production systems engineering.

We are the creators of a new fintech era!

Our mission is to change this world by making blockchain accessible to everyone in everyday life. WhiteBIT is a global team of over 1,200 professionals united by one mission — to shape the new world order in the Web3 era. Each of our employees is fully engaged in this transformative journey.

We work on our blockchain platform, providing maximum transparency and security for more than 8 million users worldwide. Our breakthrough solutions, incredible speed of adaptation to market challenges, and technological superiority are the strengths that take us beyond ordinary companies. Our official partners include the National Football Team of Ukraine, FC Barcelona, Lifecell, FACEIT and VISA.

Requirements:

We're looking for a Senior / Staff-level engineer with an open mind and a builder's instinct - someone who goes beyond executing tasks and is ready to own the High-Performance practice end-to-end: establish engineering processes, drive R&D, evaluate and champion new technologies, and lead their adoption in production.

The ideal candidate can bridge the gap between business requirements for low latency and high throughput and the realities of existing infrastructure - translating "we need to be faster" into an actionable technical roadmap. You bring a fresh perspective, know how to organize the work, and are equally comfortable optimizing a hardware parameter and presenting a strategy to stakeholders.

We expect 5+ years of hands-on experience as a DevOps/SRE/Systems Engineer in performance-critical environments - and the maturity to grow yourself and grow a team around you.

• Linux: Advanced administration and OS-level tuning — real-time OS configurations, cgroups, CPU isolation, memory management, and low-level network stack configuration.
• Hardware Internals: Deep understanding of CPU power and performance states (C/P-states), Hyper-Threading / SMT trade-offs, TX/RX ring configuration, and the full data path from NIC through PCIe to memory and CPU. Ability to reason about and tune each stage of that path under production load.
• Networking & TCP Internals: Deep knowledge of TCP/IP — congestion control, ACK behavior, buffer tuning, and protocol-level latency sources. Strong Linux networking fundamentals: low-latency NIC configuration, bypass networking technologies (DPDK, RDMA), and market connectivity (multicast feeds, co-location networks, VPNs).
• Performance Engineering: Practical experience profiling latency-sensitive systems; familiarity with tools such as perf, flamegraph, strace, tcpdump, eBPF, and hardware performance counters.
• Containerization: Deep knowledge of Docker and Kubernetes, including low-overhead deployment patterns and performance trade-offs of containerized workloads.
• IaC: Proven track record with Ansible, Terraform, and Helm in production environments.
• Monitoring: Hands-on experience with the Prometheus / Grafana stack; ability to build latency-aware dashboards and alerting (P99, jitter, throughput KPIs).
• Scripting: Solid scripting in Bash and Python; Golang is a strong plus given its role in systems tooling.
• CI/CD & Secret Management: Strong grasp of CI/CD pipelines with experience integrating performance validation stages; familiarity with Vault for secrets management.
• Security: Understanding of DevSecOps principles and Kubernetes security policies.

Key Performance Metrics You'll Work With

Latency Distribution - P99 / P99.9 across the tick-to-trade path, using hardware-level timestamps (NIC/PTP).

Jitter - variance between min/max latency, measured over large sample sets in live market conditions.

Throughput - messages per second (orders + cancels) under exchange burst scenarios.

System Reliability - сontinuous monitoring of uptime

Extra Credit / Big Pluses

• Hands-on experience with kernel-level optimization: syscall reduction, network stack bypass (DPDK, RDMA, Solarflare OpenOnload).
• Background in HFT, algorithmic trading, or other ultra-low-latency production environments.
• Experience with hardware timestamping, PTP/IEEE 1588, and precision time synchronization.
• Familiarity with colocation operations and exchange connectivity (cross-connects, proximity hosting).
• Proven SRE track record with formal SLO/error budget frameworks in latency-sensitive contexts.
• DevSecOps: vulnerability scanning, Zero Trust, RBAC, and secure delivery pipelines.
• Understanding of business processes in FinTech, crypto, or electronic trading.

Responsibilities:

• Low-Latency Infrastructure Ownership: Design, deploy, and continuously optimize infrastructure for latency-sensitive trading systems — market data handlers, order/deal gateways, and risk check engines. Own the full stack from bare metal to application delivery. Define and maintain a reference architecture for low-latency connectivity when onboarding new trading venues, including reproducible configuration baselines, validation procedures, and performance acceptance criteria — so every new venue goes live on a proven, optimized stack.
• Cloud & Hybrid Network Connectivity: Design, implement, and maintain complex low-latency network solutions for cloud provider interconnects - including cross-connects and Direct Connect / ExpressRoute links between cloud regions, hybrid on-premise/cloud topologies, and high-throughput inter-server fabrics. Ensure both latency minimization and fault tolerance at every layer: physical redundancy, failover routing, and path diversity across providers and regions.
• Hardware & OS Stack Tuning: Go deep on the full hardware and OS stack - CPU pinning, C/P-states, HT/SMT configuration, NUMA topology, IRQ affinity, TX/RX ring sizing, NIC offloading, and network stack bypass (DPDK/RDMA where applicable). Understand the complete data path from NIC through PCIe to memory and CPU - and optimize every hop.
• Infrastructure as Code (IaC): Automate provisioning and configuration of high-performance environments using Ansible, Terraform, and Helm - with performance reproducibility as a first-class requirement.
• Latency Profiling & Benchmarking: Systematically profile and debug latency hotspots across the full execution path. Establish and maintain SLA/SLO targets; track P99/P99.9 distributions, jitter, and throughput - not just averages.
• CI/CD for Performance: Build and maintain CI/CD pipelines that include automated latency regression testing, so performance degradations are caught before they hit production.
• Monitoring & Observability: Design and operate comprehensive latency monitoring and alerting systems (Prometheus, VictoriaMetrics, Grafana) - including hardware timestamping (NIC/PTP) and fine-grained performance telemetry.
• Colocation & Network Placement: Research, evaluate, and manage optimal server placements - colocations, cross-connects, proximity hosting. Work closely with network and infrastructure teams to minimize physical and logical distance to exchanges.
• High Availability & DRP: Design systems that maintain stability and reliability under peak load while continuously pushing latency lower - zero regressions, always.
• Cross-Team Collaboration: Work alongside algo developers, quant teams, and network engineers to ensure trading strategies execute with minimal infrastructure overhead.

Working terms

Immerse yourself in Crypto & Web3:

• Master cutting-edge technologies and become an expert in the most innovative industry.

Work with the Fintech of the Future:

• Develop your skills in digital finance and shape the global market.

Take Your Professionalism to the Next Level:

• Gain unique experience and be part of global transformations.

Drive Innovations:

• Influence the industry and contribute to groundbreaking solutions.

Join a Strong Team:

• Collaborate with top experts worldwide and grow alongside the best.

Work-Life Balance & Well-being:

• Modern equipment.
• Comfortable working conditions, and an inspiring environment to help you thrive.
• 24 calendar days of paid leave.
• Additional days off for national holidays.

We’re inviting a Middle Reconciliation Specialist to our team.

Ideal for someone who enjoys building and maintaining automated reconciliation processes for large volumes of transaction data from multiple sources. You’ll take ownership of turning manual checks into reliable data pipelines, automating discrepancy detection, and ensuring data quality across multi-currency operations and different markets.

Python · SQL · Data Reconciliation · ETL Pipelines · REST APIs & SFTP · Data Quality · Financial Data Analysis

Your expertise:

• 2+ years of commercial experience with Python for data processing (pandas, NumPy), with an understanding of how to work with datasets containing millions of rows without compromising performance.
• SQL: JOINs, CTEs, window functions, aggregations, and query optimization.
• Experience integrating with external data sources, including REST APIs, SFTP, and parsing various file formats (CSV, XLSX, JSON, XML, and PDF bank statements).
• Understanding of data quality principles, including validation, deduplication, handling missing values and anomalies, and ensuring data completeness.
• Experience with Git and writing maintainable code, including project structure, logging, and error handling.
• Understanding of the payments domain, including transaction lifecycle, fees, exchange rates, settlement, holds/reserves, and chargebacks.
• Experience in financial environments.
• Ability to independently manage tasks from requirements gathering through implementation and ongoing support, taking ownership of outcomes.
• English: Intermediate.

What you’ll do:

• Design, develop, and maintain transaction reconciliation pipelines across providers, bank statements, processing logs, and internal systems.
• Automate data collection from SFTP and provider admin panels, consolidating diverse data formats into a unified schema.
• Implement matching logic (1:1, 1:N, N:M), including tolerances for fees and FX differences.
• Build data quality checks and alerting mechanisms to detect discrepancies.
• Investigate discrepancies through resolution, identify root causes, fix issues at the source or logic level, and implement preventive measures.
• Prepare final operational and financial reports, including reports for month-end close.
• Align reconciliation rules with Finance, BizDev (Account Management), Risk & Compliance, and Support teams, ensuring SLA compliance.
• Document pipelines, data schemas, and business rules.

• Identify and implement process improvements, conduct code reviews, and support less experienced colleagues.

   

It would be great if you also have:

• Experience with BI tools such as Power BI, Looker Studio, or Tableau.
• Experience with chargebacks, representment, and dispute workflows.
• Experience with multi-currency reconciliation across emerging markets.

Life at Emergine:
Stay Supported

• 100% paid sick leave
• 21 working days of paid vacation
• Medical support for office and remote employees

• Benefits Cafeteria including sports, gym, healthcare, dental care, psychological support, wellness and learning.

   

Work Without Borders

• International team
• Global collaboration
• Flexible work models depending on the role

• Remote, hybrid or office-based setup depending on the position

   

Grow Through Motion

• PDP and career development plans
• Regular feedback sessions
• Leadership opportunities
• Continuous professional development
• Knowledge-sharing sessions

• Management development initiatives

   

Create Real Impact

• Ownership from early stages
• International projects
• Complex and meaningful challenges
• Collaborative management
• Open communication
• Low bureaucracy
• Space for initiative and innovation