Jobs Data Engineer

We are looking for an Engineering Manager or experienced Team Lead with a strong Data Engineering background to lead and scale the teams building our high-reliability, secure, and linearly scalable Data direction.

This role is ideal for engineering leaders who combine strong technical expertise in data and platform engineering with people leadership and delivery ownership. Whether you are already an Engineering Manager or a hands-on Team Lead looking to expand your impact, you will play a key role in shaping teams, data architecture, and engineering culture.

You will lead the company's transition toward an AI-first approach - ensuring fast, governed access to analytics and AI/ML models while maintaining strict financial and regulatory compliance. You will work at the intersection of data engineering, platform/infrastructure, and business, with a strong focus on reliability, performance, scalability, security, and data governance.

Responsibilities

• Lead and develop cross-functional Data Engineering and Data Platform teams
• Own delivery, quality, and technical outcomes for the data platform
• Partner closely with Product, Analytics, Data Science/ML, and Senior Engineering leadership to translate business and AI requirements into a scalable platform roadmap
• Drive and evolve data engineering processes, including planning, estimation, CI/CD for data, testing and data quality, code reviews, and release management
• Contribute to architectural decisions for data systems running on hybrid infrastructure (batch + streaming, lakehouse, and beyond)
• Ensure high availability, performance, data reliability, and observability of production pipelines and platform services
• Embed security, privacy, data lineage, and regulatory compliance into the platform by design
• Identify and reduce technical and data debt while maintaining delivery velocity
• Mentor engineers, support career growth, conduct 1:1s and performance conversations
• Participate in hiring and team scaling initiatives
• Foster a culture of ownership, collaboration, data quality, customer focus, and continuous improvement

Requirements

• 5+ years of hands-on professional Data Engineering experience, with a strong track record of building and operating production-grade data pipelines and platforms
• Experience leading engineering teams as an Engineering Manager, Team Lead, or similar leadership role
• Strong understanding of modern data architectures, including data modeling, ETL/ELT, distributed data processing, pipeline orchestration, batch and streaming workloads, and data storage
• Experience delivering complex data systems or platforms in production environments
• Background in financial systems, FinTech, or other high-reliability, regulated domains
• Strong understanding of data platform and backend systems - data modeling, distributed data processing, and pipeline orchestration
• Hands-on experience with the modern data stack: batch and streaming processing, orchestration, and warehousing/lakehouse 
• Knowledge of CI/CD pipelines, infrastructure automation, and deployment strategies for data workloads
• Familiarity with data observability (data quality, metrics, logging, alerting, lineage) and performance monitoring

Nice to Have

• Experience building platforms that serve AI/ML workloads (feature stores, MLOps, model-serving, vector/embedding stores)
• Experience with low-latency or high-throughput data systems
• Exposure to trading, fintech, or market-data platforms
• Experience with data governance, security, and regulatory compliance (PII handling, audit, lineage) in financial environments
• Experience scaling data and engineering teams and organizations

We are looking for a 🧑‍🤝‍🧑human-first Python engineer to join our global delivery team.  In the age of AI, exceptional customer communication, empathy, and proactive problem-solving matter far more than technical excellence. We value people who listen, understand customer needs, think architecturally, take ownership, and collaborate naturally.  The role involves coordinating customer data engineering requests, resolving ETL/scraper issues, and designing and maintaining data pipelines.  Fluent English and basic to intermediate Python skills are required. We hire for empathy, communication, and architectural thinking first. Technical skills come second. We'll help you grow technically as far as you can go.

Company Description

Are you a Senior Data Engineer passionate about building scalable, high-performance data platforms and working with modern Lakehouse technologies? Join Sigma Software’s Data Engineering Center of Excellence and contribute to the modernization of an enterprise-scale analytics ecosystem for the retail domain.
We are looking for a Senior specialist with strong Databricks, PySpark, and cloud data engineering expertise to participate in the migration of a large-scale analytical platform from BigQuery to Databricks. You will collaborate with international teams, contribute to architectural decisions, and help shape reliable and scalable data solutions.
We at Sigma Software create opportunities for continuous learning, technology growth, and meaningful engineering impact while working on complex international projects.

CUSTOMER

Our Customer is a leading retail technology company specializing in AI-driven pricing optimization solutions for enterprise retailers. The company helps businesses improve profitability and competitiveness through advanced analytics, automation, and intelligent pricing strategies. Their platform combines business intelligence with sophisticated algorithms to support data-informed pricing decisions at scale for global retail organizations.

PROJECT

The project focuses on the strategic migration of a large-scale analytical platform from a legacy BigQuery ecosystem to a modern Databricks Lakehouse architecture. The platform processes high-volume retail datasets, machine learning workloads, analytics pipelines, and customer-specific business logic.
As part of the modernization initiative, the engineering team is implementing scalable Spark-based processing, Delta Lake architecture, medallion data layers, and modern governance practices. The role offers an opportunity to work with distributed data processing systems, optimize large-scale workloads, and contribute to the evolution of an enterprise-grade data platform.

Key Technologies: Databricks, Apache Spark, PySpark, Delta Lake, Python, SQL, Airflow, GCP, CI/CD, Unity Catalog

Job Description

• Participate in the migration of a large-scale analytical platform from BigQuery to Databricks
• Design and implement scalable Lakehouse architectures using Databricks and Delta Lake
• Analyze existing ETL / ELT workloads and define migration approaches
• Develop and optimize data pipelines processing large volumes of retail and analytical data
• Implement incremental processing strategies and scalable transformation frameworks
• Build and maintain Spark-based data processing solutions using PySpark
• Design and maintain medallion architecture layers including Bronze, Silver, and Gold
• Implement data governance and security best practices using Unity Catalog
• Collaborate with Data Science, Analytics, Product, and Customer Engineering teams
• Participate in architecture discussions and technical solution design
• Develop reusable data platform components and engineering standards
• Conduct code reviews and contribute to platform reliability and maintainability
• Troubleshoot and optimize complex SQL and Spark workloads
• Support production deployments and platform modernization activities

Qualifications

• 5+ years of professional experience as a Data Engineer
• Strong programming skills in Python and advanced SQL
• Hands-on commercial experience with Databricks
• Strong knowledge of Apache Spark, primarily PySpark
• Experience designing and building modern cloud-based data platforms
• Experience developing ETL / ELT pipelines and large-scale data processing solutions
• Hands-on experience with Delta Lake
• Experience with Spark Declarative Pipelines
• Experience with cluster monitoring, metrics analysis, and performance optimization
• Strong understanding of distributed data processing architectures
• Solid understanding of data warehousing concepts and dimensional modeling
• Experience with Airflow or similar orchestration tools
• Experience optimizing complex analytical SQL workloads
• Experience implementing CI / CD practices for data engineering platforms
• Strong troubleshooting and performance optimization skills
• Ability to work collaboratively in cross-functional international teams
• Upper-Intermediate or higher English level

WILL BE A PLUS

• Experience working with GCP cloud services
• Experience with AWS or Azure cloud platforms
• Experience in retail analytics or pricing optimization domains
• Experience supporting machine learning or AI-related data workloads
• Experience with platform modernization and cloud migration initiatives

Additional information

PERSONAL PROFILE

• Strong analytical and problem-solving mindset
• Proactive and ownership-driven approach
• Ability to work independently and collaboratively
• Good communication and stakeholder collaboration skills
• Passion for scalable data engineering and modern data platforms
• Interest in continuous learning and technology innovation

Start: immediately
Location: Remote, EU, Ukraine
Workload: full-time, 40 h/week
Start: ASAP
Language: English min. B2
Experience: 7 Years+
Contract: flexible; 6 months fixed, option to convert to permanent
Rate: open to proposals

CONTEXT
An agentic enterprise AI platform, currently running in the cloud (per customer).
They are building an on-prem version for customer / colocation data centers.
This is a founding engineer role for the on-prem product: the candidate builds the tooling so customers can install, upgrade, back up, restore and troubleshoot the platform without Kubernetes knowledge.

MUST-HAVE

• Kubernetes in production (strong, hands-on)
• Writing and maintaining Kubernetes operators (CRDs, controllers)
• Building a CLI / lifecycle tooling: install, configure, upgrade, scale, backup, restore
• Distributed, microservice-based systems
• Platform engineering / developer tooling / internal infra products
• Load, stress and resilience testing, performance baselines and sizing
• Operational security, secure deployments, incident response
• Strong in at least 2 programming languages (Go is a natural fit for operators/CLI)
• Product mindset, ownership, good communication (English, German is a plus)
• Lead-level seniority: able to shape technical decisions

NICE-TO-HAVE

• SUSE Rancher, k3s
• On-prem deployments in customer environments
• Azure
• .NET 10, ASP.NET Core, Aspire
• Self-hosted / customer-operated products, installers, appliances
• Security-sensitive or regulated environments
• Functional programming / formal verification

CANDIDATE PROFILE
Senior/Lead, ideally 7+ years, with clear on-prem Kubernetes experience
Available to start immediately

Workload — Full-time
Duration — short-- About 2 months
Location — Romania
Rate up to — 25 USD
Type of the request — Search

📝 Примечание:
We’re looking for two Senior Data Engineers for a short, focused project on a healthcare (pharmacy) patient platform. The core goal is to build and validate a data migration tool that will move patient data from a legacy system to a new platform. The full-scale migration happens later. This phase is about building the tool and proving it on a few test instances. There is also some integration and feature work around refills, messaging, and appointments.

What you’ll do
● Design and build a reusable migration tool for patient records, messages, and appointments
● Define field mappings, transformation logic, and migration sequencing for a legacy source with weak relational structure
● Run test migrations on a small number of instances and produce results reports
● Analyze source data to spot and handle data quality issues, and validate accuracy and completeness
● Document the tool and process so the full migration can be run reliably later
● Support backend integration work, such as a fax-based fallback for refill requests and related admin configuration What you bring
● 8+ years of experience in data engineering, including building migration or ETL tooling
● Strong Python skills for data processing, transformation, and analysis
● Solid Node.js experience for backend services and integrations
● Strong SQL and experience working with messy or loosely structured legacy data
● Experience handling sensitive or regulated data (healthcare/HIPAA is a plus)
● Clear communication and the ability to deliver independently on a fixed timeline

Nice to have
● Experience with patient portals, pharmacy, or healthcare systems
● Familiarity with product analytics or data warehouse tools

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.

About Us

True Path Leads  is a fast-growing performance marketing company building high-load infrastructure for lead generation at scale. We process millions of events daily across complex integrations with external partners. Quality is critical — and we're looking for a Senior Manual QA Engineer who can own it. 

What We're Looking For

We are looking for an experienced Senior Data Engineer to design, build, and maintain scalable data infrastructure supporting analytics, reporting, machine learning, and business operations.

The ideal candidate has strong experience with data pipelines, ETL/ELT processes, distributed systems, and database optimization, with a focus on reliability, performance, and scalability.

Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build and optimize ETL/ELT workflows for data ingestion, transformation, and delivery.
• Develop and maintain data architectures supporting analytics, BI, and machine learning.
• Implement and maintain streaming data pipelines using technologies such as Kafka and orchestration tools such as Airflow.
• Design and optimize data models, schemas, and storage solutions across SQL, NoSQL, and analytical databases.
• Ensure data quality through validation, deduplication, normalization, and reconciliation.
• Optimize data pipelines for performance, scalability, latency, and cost efficiency.
• Implement monitoring, logging, alerting, and fault-tolerance mechanisms.
• Troubleshoot production issues involving databases, pipelines, and distributed systems.
• Maintain data contracts, schema compatibility, data lineage, and documentation.
• Implement data security best practices, including access controls, encryption, and PII protection.
• Collaborate with engineering, analytics, product, and ML teams to deliver reliable data solutions.
• Automate infrastructure deployment and maintenance using CI/CD and Infrastructure as Code.
• Participate in architectural decisions and continuously improve data platform reliability and maintainability.

Requirements

• Strong commercial experience in Data Engineering.
• Hands-on experience designing and maintaining scalable data pipelines.
• Strong knowledge of ETL/ELT processes and data integration.
• Experience with batch and real-time data processing.
• Experience with Kafka, Airflow, or similar technologies.
• Strong understanding of relational, NoSQL, and analytical databases.
• Experience with data modeling, schema design, and database performance optimization.
• Understanding of distributed systems, fault tolerance, and high-availability architectures.
• Experience implementing data quality checks, monitoring, and observability.
• Familiarity with CI/CD practices and Infrastructure as Code.
• Knowledge of data security, access management, and sensitive data protection.
• Strong troubleshooting, analytical, and problem-solving skills.
• Ability to collaborate effectively with cross-functional teams.
• Languages: English B2, Russian or Ukrainian — native.

• Located in a European time zone.

   

Nice to Have

• Experience building data infrastructure for machine learning workloads.
• Experience defining SLIs/SLOs for data pipelines.
• Knowledge of data governance, lineage, and compliance practices.
• Experience with capacity planning and infrastructure cost optimization.
• Experience improving or modernizing existing data platforms.

What You'll Get

• Fully remote position within a European timezone.
• Work on high-load, real-world systems with complex data flows.
• Direct collaboration with engineering and product teams.
• Fast-paced startup environment with experienced leadership.
• Competitive compensation based on experience and skill set.
• Clear growth path in a scaling product company.

Important: after confirming your application on this platform, you’ll receive an email with the next step: completing your application on our internal site, LaunchPod. So keep an eye on your inbox and don’t miss this step — without it, the process can’t move forward.

Why join us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! :)

About the role

We are looking for a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

The mandatory requirements are 2+ years of experience with Databricks and GCP, advanced SQL, and experience with Delta Lake and lakehouse architectures, along with an upper-intermediate English level. Experience building AI agents is a plus.

What you will do

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
• Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and processing solutions to support new business requirements and datasets.
• Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
• Implement data quality checks, monitoring, validation, exception handling, and production controls.
• Optimize PySpark and SQL workloads for performance, reliability, and scalability.
• Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
• Troubleshoot complex data and production issues and implement sustainable solutions.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.

Must haves

• 2+ years of strong hands-on experience with Databricks and PySpark.
• Advanced SQL and data-processing skills.
• Hands-on experience with GCP, particularly BigQuery.
• Experience with Delta Lake and modern data lake/lakehouse architectures.
• Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
• Experience building reliable, scalable, production-grade data solutions.
• Strong analytical and troubleshooting skills.
• Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.
• Upper-intermediate English level.

Nice to haves

• Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.
• Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.
• Familiarity with orchestration and automation frameworks.
• Experience developing reusable data engineering frameworks and platform components.
• Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.

Perks

• Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
• Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
• Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
• Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
• Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
• Well-being & support: access local well-being programs and people-focused support tailored to your location

Meet Our Recruitment Process

Asynchronous stage – An automated, self-paced track that helps us move faster and give you quicker feedback:

• Short online form to confirm basic requirements
• 30–60 minute skills assessment via Codility – a platform founded in Poland that helps us provide quicker feedback and streamline this stage of the process.
• 5-minute introduction video

Synchronous stage – Live interviews

• Technical interview with our engineering team (scheduled at your convenience)
• Final interview with your future teammates

If it’s a match — you’ll get an offer!

We are looking for a Senior Data Engineer / AI Specialist (Agentic AI, AWS) to work on an enterprise AI platform MVP. You will build the data and AI layer: pipelines from multiple enterprise sources, a data lake, agentic AI use cases and predictive models. 

Responsibilities 

• Design and build ETL/ELT pipelines from heterogeneous enterprise sources (ERP, planning, cost and schedule data, documents, external price indices)
• Build and maintain the data lake / lakehouse; ensure data quality, modeling and governance
• Develop agentic AI solutions: multi-agent orchestration, LLM integrations (AWS Bedrock), RAG over unstructured documents such as contracts
• Build classical ML models, for example forecasting cost at completion from historical data
• Evaluate AI output quality and reliability (evals, guardrails)
• Ensure solutions comply with ISO 27001 / SOC 2 data security requirements
• Work closely with the full-stack developer and the client’s product and AI teams

Requirements 

• 5+ years in Data Engineering and/or Applied AI
• Strong Python and SQL
• Practical experience with LLM applications: agents, RAG, prompt engineering, vector search, document processing
• AWS: S3, Bedrock, data services; PostgreSQL
• Experience with classical ML (regression, time-series forecasting)
• Pipeline orchestration and data quality practices
• Fluent English; availability for Central Europe / Middle East hours

Nice to have 

• Databricks (likely platform for ETL and ML on this project), PySpark. Snowflake is also relevant
• MLOps (model deployment, monitoring)
• Experience with AI governance and data readiness

• Domain knowledge of capital projects, cost management or contracts

   

We Offer 

• Innovation Culture: Work in an AI-first team that actively experiments with agentic AI and modern development approaches 
• Meaningful Challenge: Build the data and AI foundation of an enterprise AI platform, from the data lake to production-ready AI agents and predictive models 
• Flexible Work: Remote work or a modern office in Kyiv 
• Generous Time Off: 20 paid vacation days + 15 sick leave days annually 
• Professional Growth: Compensation for courses, certifications (including AWS and Databricks), and learning resources 
• Cutting-Edge Tools: Access to premium AI tools (Cursor Pro, Claude Code, GitHub Copilot, etc.) 

• Supportive Environment: Collaboration, trust, and focus on outcomes rather than micromanagement

   

Recruitment Process 

1. HR Interview 
2. Tech & Final Interviews 
3. Job Offer 

About Zoral

Zoral is an IT product and professional services company serving banks, insurers, wealth managers and fintechs. Alongside its flagship product, Zoral fOS (Financial Operating System), Zoral delivers data, analytics and AI programmes for regulated financial-services firms in Europe, the UK, the US and Asia.

About the role

We are looking for a Data Architect / Senior Data Engineer to define the target architecture and actively design, develop and deliver enterprise data platforms on Azure Databricks for regulated financial-services clients. Typical platforms integrate tens of source systems and millions of customer and policy records, support several hundred regulatory, management and operational reports, and are delivered by growing multidisciplinary teams of data engineers, architects, analysts and client specialists.

This is a hands-on architecture and engineering role within a collaborative delivery team. You will assess the client’s existing data landscape, help refine the target architecture, establish modelling and engineering standards, and act as a senior technical authority throughout delivery. You will work closely with other architects and engineers, providing direction, reviewing designs and supporting implementation rather than operating as a standalone architect.

At the same time, you will remain close to the engineering work: developing transformation logic and data models, profiling and cleansing data, implementing data-quality controls, and contributing to production engineering where your experience has the greatest impact. The successful candidate should be comfortable moving between architecture, technical leadership and hands-on implementation while enabling the wider team to deliver effectively.

Responsibilities

• Assess existing data lakes, warehouses, pipelines, models, jobs, notebooks and tooling; work with client architects and the wider delivery team to confirm or refine the target architecture and document decisions, options, trade-offs and rationale.
• Design the layered data architecture (landing, bronze, silver and gold) and guide its implementation across the engineering team, covering ingestion, standardisation, transformation, integration, conformance, data products and reporting marts.
• Develop and review production-grade data transformations using SQL and Python/PySpark, including reusable and metadata-driven pipelines that are idempotent, re-runnable, testable and observable.
• Define data-modelling standards and contribute to the development of integrated and consumption-layer models, including Data Vault 2.0, Kimball dimensional models, slowly changing dimensions, historical and as-at reporting, and model-generation approaches.
• Define the data-quality and data-cleansing approach together with the engineering team: establish profiling and baseline measures; define rule catalogues, quality gates and scorecards; design quarantine, remediation, reconciliation, audit, balance and control processes; and ensure these are implemented consistently.
• Guide and contribute to data cleansing, standardisation, de-duplication, customer matching and entity-resolution approaches, including match rules, survivorship and persistent identifiers.
• Design how the new platform is separated from and coexists with legacy platforms, including catalogue and environment layout, access boundaries, dependencies, migration, reconciliation and decommissioning.
• Define practical standards for layering, naming, ownership, classification, lineage, security, masking, resilience and retention, and work with engineers to ensure these standards are applied consistently in delivery.
• Evaluate tools for ingestion, transformation, orchestration, data quality, catalogue, lineage, master data and reporting, working with other technical stakeholders to present recommendations and trade-offs.
• Review engineering designs and code, contribute directly to complex or critical implementation areas, troubleshoot difficult data and pipeline issues, and support automated testing and CI/CD practices.
• Provide technical leadership to engineers and client staff through design reviews, pairing, mentoring and collaborative problem solving.
• Produce architecture documents, decision records, technical standards, runbooks and handover material in collaboration with the wider project team.

Requirements

• 8+ years in data architecture and/or senior data engineering, with substantial hands-on delivery responsibility, including at least three years designing and engineering Databricks platforms in production.
• Demonstrated ability to operate as both an architect and a senior engineer within a delivery team: define target-state designs and standards, guide other engineers, and implement or materially contribute to pipelines, transformations and data models.
• Experience providing technical leadership within multidisciplinary data teams, including design reviews, mentoring, engineering standards and collaborative delivery.
• Expert SQL and strong Python/PySpark skills, with experience developing, testing, optimising and supporting production data transformations.
• Hands-on knowledge of Azure Databricks, including Unity Catalog governance (grants, row filters, column masks, tags and lineage), Delta Lake, Lakeflow Declarative Pipelines, Lakeflow Jobs and Lakeflow Connect.
• Proven experience designing and developing layered lakehouse or data-warehouse architectures using Data Vault 2.0 and Kimball dimensional modelling, including slowly changing history and as-at data.
• Experience defining and implementing data-quality and cleansing strategies, including profiling, rule design, quality gates, monitoring, scorecards, quarantine, remediation and reconciliation controls.
• Experience with master data management, customer matching, de-duplication or entity resolution.
• Experience migrating data, pipelines and models from legacy warehouses or lakes, including coexistence, reconciliation, parallel running and decommissioning.
• Practical experience with Git, code review, automated testing, CI/CD and production support for data pipelines.
• Ability to write clear architecture and engineering documentation and present technical decisions to senior stakeholders.
• Excellent written and spoken English; willingness to work on site at client premises, including in the UK, for periods of several weeks.

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client: 
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Its mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We are looking for an experienced and dynamic Data Engineer with a curious and creative mindset to join our client’s team. The ideal candidate will have a strong background in Python, SQL and large-scale data pipelines. 

As a Data Engineer you will design, build and operate the data platform that powers the company: pipelines that ingest and transform very large datasets from external data partners, data models that the whole company queries, and services that make that data available to internal products. 
 

Location: Ukraine is mandatory.

Responsibilities:
-Design and build batch data pipelines that ingest, validate and transform multi-billion-row datasets from external data providers and internal systems;
- Model complex real-world data: dimensional models, reference data, and temporal data whose attributes change over time (e.g. slowly changing dimensions), and evolve those models safely as upstream sources change their schemas and semantics;
- Develop and operate workloads on our lakehouse platform (Databricks / Spark / Delta) and our data warehouse (Redshift), including migrating existing pipelines from the warehouse to the lakehouse;
- Orchestrate pipelines with Airflow: scheduling, dependencies, retries, backfills and alerting;
- Prove correctness, not just completion: design parity checks and reconciliation queries when replacing an existing pipeline, run large historical backfills, and investigate data discrepancies down to the row level;
- Build and maintain Python services and REST APIs that serve data to internal products;
- Optimize for performance and cost: query tuning, table design, workload management and right-sizing compute;
- Own what you ship: monitor production pipelines, participate in incident triage and root-cause analysis, and harden systems so the same failure does not happen twice;
- Collaborate cross-functionally with product managers, data scientists and stakeholders across the company to deliver on product roadmap;
- Work within an Agile team that releases cutting-edge new features regularly;
- Take a high degree of ownership and freedom to experiment with new technologies to improve our software.

Requirements:
- Python as your primary language for pipelines and services in recent years;
- 3+ years of hands-on Spark (PySpark) in production batch pipelines: you wrote the jobs, deployed them through CI/CD and kept them running at scale;
- Production experience with Databricks: Jobs/Workflows, Delta Lake, and deploying through CI/CD. Notebook-only experience does not count. A strong candidate without Databricks must show deep production PySpark on AWS EMR instead;
- Expert-level SQL: writing, reading and tuning complex analytical queries against very large tables, and debugging why two result sets disagree;
- Amazon Redshift in production is strongly preferred. Otherwise, deep hands-on tuning of another MPP columnar warehouse: distribution and sort keys, workload management, and diagnosing slow queries from the query plan;
- Built and owned production Airflow DAGs: dependencies, retries, backfills and alerting;
- Ability to design complex data models: normalized, dimensional and temporal (slowly changing dimensions, effective-dated records, point-in-time correctness);
- Experience integrating third-party data feeds: schema drift, late or missing deliveries, vendor data-quality defects and versioned reference data;
- Experience building REST services in Python (FastAPI, Flask, etc.);
- Experience developing, maintaining and debugging large server-side code bases;
- Working knowledge of AWS (S3, IAM, ECS) and Docker;
- Good engineering practices: unit and integration tests, code review, CI/CD;
- The desire to take a high level of ownership, learn quickly, keep a high bar for quality and be pragmatic;
- Able to communicate effectively with U.S.-based teams and work 11:00 AM – 8:00 PM EEST (11:00–20:00).

Tech Stack:
- Almost everything we run is on AWS (S3, ECS, EMR, RDS and more)
- Python is our primary language; SQL is everywhere;
- Databricks (Spark, Delta Lake) is our lakehouse platform; - Redshift and Postgres are our warehouses and operational databases;
- Airflow orchestrates our pipelines;
- Docker for packaging; GitHub Actions and Jenkins for CI/CD;
- Grafana, Sentry and OpenSearch for observability;
- Datasets measured in billions of rows.

We are looking for an experienced Senior Data Engineer / Analytics Engineer to join an established, long-term project within the private sector.

The role combines hands-on data engineering with analytics engineering, focusing on building and maintaining scalable data pipelines, developing data models, and supporting business intelligence solutions.

The ideal candidate has strong experience with Google Cloud Platform (GCP), BigQuery, Python, and SQL, and can independently deliver reliable, production-ready data solutions.

Requirements

Core Requirements — High Priority

1. 5+ years of experience in Data Engineering, Analytics Engineering, or similar roles.
2. Google Cloud Platform (GCP) & BigQuery — strong hands-on experience developing and maintaining cloud-based data solutions.
3. Python & SQL — advanced proficiency in building, optimizing, and maintaining production data pipelines.
4. Apache Airflow — practical experience with workflow orchestration, scheduling, and monitoring data pipelines.
5. ETL/ELT & Data Warehousing — solid understanding of data integration, transformation processes, and data warehouse architecture.

Analytics & BI — Medium Priority

1. Data Modeling — experience with dimensional modeling, curated data models, and semantic layers in BigQuery.
2. Power BI — experience developing semantic models, datasets, and business intelligence reports.
3. API Integrations — experience integrating external data sources and third-party APIs into data pipelines.

Infrastructure & Engineering Practices

1. Docker & Google Cloud Run — experience deploying, managing, and troubleshooting containerized workloads.
2. Git & CI/CD — experience with version control, code reviews, automated deployments, and maintainable code practices.
3. Documentation & Collaboration — ability to work independently, document technical solutions, and share knowledge with the team.

Responsibilities

• Design, develop, and maintain production-grade data pipelines using Python and SQL.
• Build and optimize data processing solutions on GCP and BigQuery.
• Develop and manage data orchestration workflows using Apache Airflow.
• Integrate external data sources through third-party APIs.
• Design and maintain curated data models, dimensional models, and semantic layers in BigQuery.
• Develop and support Power BI semantic models and reporting solutions.
• Deploy, monitor, and troubleshoot containerized workloads using Docker and Google Cloud Run.
• Ensure data quality, pipeline reliability, and performance.
• Follow engineering best practices, including Git workflows, CI/CD, code reviews, and technical documentation.
• Collaborate with internal data and business teams to support ongoing analytics initiatives and the BI roadmap.

Project Details

This is an opportunity to join an existing, long-term private-sector project with an established cloud-based data platform.

We're rebuilding Headway Inc's IT Infrastructure team, and we're looking for a System Engineer to help move it from ad-hoc requests to stable systems and automation. You'll take ownership of key projects in device management, access and software licenses, and see them through from start to finish. This role suits someone who wants real autonomy and visible impact.

About Headway Inc

Headway Inc is a global tech company building products that help people grow. Our apps have reached 170M+ downloads worldwide, and TIME ranked Headway Inc #4 among the World's Top EdTech Companies.

Our achievements start with our people and the values we share. At Headway Inc, we believe in building strong teams around our SELECT values.

Behind every product we ship are around 500 people who need their devices, access and tools to simply work. IT Infrastructure makes that happen: the right setup on day one, no friction in daily work, and offices that keep running even through blackouts.

What you'll do

• Device management: Lead the rollout of device management for our Mac fleet, so every laptop is secure and ready to use from day one.
• Software licenses: Build one clear process for all SaaS and AI tools, from requests to renewals and cost control.
• Access and collaboration platforms: Administer our core workplace tools and make access to them simple and secure.
• Office infrastructure: Keep our office network, servers and backups reliable, including through blackouts.
• Employee lifecycle and automation: Make onboarding and offboarding smooth and automate repetitive IT work.
• Helpdesk and knowledge: Support colleagues together with the team and turn what you know into documentation everyone can use.

You'll report to Inna Selivon, our Administrative Manager, and work closely with your IT Infra teammates and with teams across the company, from people operations and project management to engineering and security.

What success looks like

• Key infrastructure projects, starting with device management, have a clear owner and are brought to completion.
• Every Mac is managed and secure, and new laptops are ready to use from the first login.
• Software licenses are transparent: every tool has an owner, requests go through one flow, and unused seats are regularly cleaned up.
• Access to core apps is centralized and secure, and onboarding and offboarding run smoothly with minimal manual work.
• Critical systems are documented, and the team can operate each of them confidently.
• The team spends more of its time on projects and automation. Depending on your results and interests, you can grow towards a Senior role or take the lead on technical areas as the team grows.

You'd be a great fit if you

• Have experience in system engineering or corporate IT.
• Have hands-on experience rolling out an MDM for macOS (ideally Jamf) and setting up SSO and MFA.
• Administer Google Workspace and manage software licenses and subscriptions.
• Are confident with office networks, VPN, servers, backups and DNS.
• Take an AI-first approach to automation: when you see repetitive work, you reach for AI tools first and back them up with scripts and Jira automations.
• Take ownership of projects and close them end to end: plan the stages, spot risks early and keep stakeholders informed.
• Enjoy building processes, treat helpdesk as part of the job, explain technical things simply and document what you know.
• Speak English at B2+ level, since you'll work with vendors, documentation and licensing terms.
• Align with our team values, SELECT, and put them into practice in the way you work, make decisions and collaborate with others.

It would be a plus if you also have:

• Mentoring or informal tech-lead experience.

What we offer

• Two week-long paid corporate holidays each year and 20 vacation days.
• Medical insurance, full sick leave compensation and access to a corporate doctor.
• A personal development plan, professional communities and a knowledge base.
• Compensation for English-language learning, external training and courses.
• An office with a reliable shelter, generators, satellite internet, and other amenities.
• Sports activities, including running, yoga, boxing and more.
• Supporting initiatives that help Ukraine.

We've created a Talent Guide to help you navigate our hiring process with clarity and confidence — and get the most out of each stage. Take a look here: https://makeheadway.notion.site/Headway-Inc-Talent-Guide-25e16bdf9fd280a59655d35ba4d57954?pvs=74

Interested? Send us your CV — we'd love to hear from you.

Evowill is looking for a Junior Data Scientist who is passionate about data analysis, machine learning, and extracting insights from data, and eager to grow in a supportive and dynamic environment. We offer a clear path: starting with a hands-on internship and transitioning into a commercial project role — 100% of our juniors join real projects.

Requirements:

• Knowledge of Python
• SQL (MySQL / PostgreSQL)
• Basic understanding of Machine Learning (scikit-learn)
• Basic data analysis skills (Pandas, NumPy)
• Solid statistics foundations
• Git, GitHub/GitLab
• English: Intermediate
• Pet projects (data analysis / ML) 
• Fast learner, organized, responsible
   

Would be a plus:

• Quantitative Science (Math, physics, statistics) or Engineering with ML or modeling majors (3rd year or higher)
• Experience with statistical or numerical modeling or having participated in data related research teams
   

Internship Stage (3 months):

• Full-time, practical learning
• Pet projects, technical interview preparations, and soft skills training
• English Speaking Club
• Senior Developers mentors from Ukraine and Colombia
• Technologies: Core Data Analysis and Modeling, Python libraries, as well as learning to apply modern ML techniques to real-world data.

Project Stage:

• Join commercial projects as a Data Scientist
• Salary from $1000 with regular reviews
• Ongoing mentorship and technical support
• Optional English club and extra tech learning

ZentixSoft is looking for a Data Architect! 🚀

Format: Full-Time Outstaffing, long-term contract (12 months), 100% Remote.

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Role Overview & Responsibilities:

We are looking for a hands-on Data Architect to take full architecture ownership of a complex data platform. In this role, you will assess, stabilize, improve, and modernize the existing architecture, delivering rapid, actionable engineering recommendations.

• Architecture Ownership: Define tactical (immediate stabilization) and target (long-term modernization) architectures that engineering teams can directly execute.
• Platform Modernization: Assess complex, legacy data environments; identify performance/scalability bottlenecks; and deliver clear, implementation-ready architectural outputs.
• Unstructured & Structured Data: Architect data pipelines and modeling frameworks capable of handling unstructured data (documents, PDFs, free text, scanned content, images) and structured/file-based datasets.
• Tech-Neutral Evaluation: Evaluate enterprise technologies based on scale, cost, workload, and business requirements rather than preference (determining when Databricks, Synapse, ADF, or Azure SQL are truly justified).
• Data Modeling & Pipelines: Design robust ETL/ELT frameworks, dimensional schemas, star schemas, and incremental processing/refresh strategies.
• Documentation & Governance: Create detailed architecture diagrams, technical decision logs, and data governance standards.
   

🎓 Our Perfect Match (Requirements):

• Proven Architecture Ownership: 4+ years of recent, demonstrated experience as a Data Architect owning overall Azure Data Platform architectures (candidate must be an Architect, not solely a Data Engineer).
• Azure Data Platform Expertise: Extensive hands-on background with Azure Data Services, including Azure SQL / SQL Server, Azure Data Lake, Azure Data Factory (ADF), Azure Synapse, and Databricks.
• Azure Cosmos DB (MUST-HAVE): Hands-on experience working with Azure Cosmos DB and file-based data stores within enterprise environments.
• Data Engineering Core: Strong mastery of Python, SQL, ETL/ELT, and Lakehouse architectures.
• Unstructured Data Experience: Proven track record handling unstructured data sources (documents, PDFs, text, scanned files).
• Fast Delivery & Problem Solving: Ability to onboard quickly into complex environments, navigate incomplete documentation, and deliver meaningful architectural guidance rapidly.
• Communication & English: Fluent English (C1) with exceptional technical writing and stakeholder communication skills.
   

📍 Project Details:

⏳ Duration: 12 Months (Full-time, 100% Remote).

🕒 Time Zone: CET / CEST.
 

We look forward to receiving your CV (please ensure your CV details your project contexts, architecture ownership, and hands-on Cosmos DB/Azure experience).