Jobs Data Engineer

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to strengthen our data foundation through good modeling, clean schemas, reliability and automation.

• Responsibilities:

  Work closely with product teams to help define and execute technical criteria
  Architect and maintain robust data ingestion pipelines that feed both our application and analytics
  Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data
  Help onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with our existing services and workflows
  Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema change handling
  Build a documented, well-tested data foundation that serves the app, analytics and future AI use cases

• Mandatory Skills Description:

  Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous IC
  Designing and building production ingestion pipelines from APIs, files and shared-data sources, using a workflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)
  Production experience with a cloud data warehouse (Snowflake preferred)
  Strong SQL, plus production experience in Python (preferred) or Node.js/TypeScript
  AWS or another enterprise cloud platform
  AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD
  Strong English and communication skills

• Nice-to-Have Skills Description:

  dbt
  Preparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)
  Infrastructure as code (e.g. Terraform)
  Exposure to energy, utilities or sustainability data

Project Description:

You will join a Regulatory Reporting Platform within a large US investment bank. The platform is responsible for collecting, processing, and transforming large volumes of data from multiple internal systems to produce regulatory reports for 240 jurisdictions worldwide. In total, the platform supports around 10,000 reports across a wide range of regulatory, compliance, financial, and risk reporting areas.
This is a large-scale, business-critical data platform operating in a highly regulated environment, where data accuracy, quality, traceability, and timely delivery are essential. The team works on building and enhancing data pipelines, implementing reporting logic, ensuring data validation and reconciliation, and supporting the generation of complex regulatory outputs.
The project is delivered by a global distributed team with colleagues located in Ukraine, Poland, and the US, offering close collaboration with engineers, analysts, and business stakeholders across multiple regions.

Responsibilities:

- Design, develop, and maintain solutions for a large-scale Regulatory Reporting Platform
- Build and enhance data processing pipelines using Java and Apache Spark
- Implement scalable logic for data ingestion, transformation, aggregation, and enrichment from multiple internal data sources
- Develop and optimize batch processing jobs to support the generation of regulatory reports across multiple jurisdictions
- Ensure high standards of data quality, validation, reconciliation, and consistency throughout the reporting lifecycle
- Translate business and regulatory requirements into robust technical solutions
- Collaborate with business analysts, QA engineers, and cross-functional teams to deliver reporting functionality
- Investigate and resolve data and application issues in development, test, and production environments
- Improve application performance, scalability, and reliability for high-volume data processing workloads
- Contribute to the design and implementation of reusable components and engineering best practices
- Participate in code reviews, testing, and release activities to ensure high-quality deliverables
- Work closely with distributed teams across Ukraine, Poland, and the US in an agile delivery model
- Support continuous improvement of the platform, including monitoring, troubleshooting, and operational stability

Mandatory Skills Description:

- 6+ years of commercial experience with Python
- 2+ years of PySpark experience
- Experience in designing and developing scalable, high-performance backend applications
- Good knowledge of SQL and working with large datasets from multiple data sources
- Strong problem-solving skills and ability to work effectively in a global distributed team

Nice-to-Have Skills Description:

- Experience in financial services, investment banking, or regulatory reporting projects
- Familiarity with data quality, reconciliation, and control frameworks
- Experience working with large-scale enterprise data platforms
- Knowledge of cloud environments and modern data platform architectures
- Understanding of CI/CD practices and automated deployment pipelines
- Experience working in Agile / Scrum teams within a distributed global setup

Languages:

English: B2 Upper Intermediate

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!

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

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 looking for a Data Engineer (3+ years of experience) to join a project for a client in the energy industry.

What you’ll do

• Design, develop, and improve data pipelines within an Azure Data Platform
• Integrate new data sources into existing Data Lake and analytics environments
• Work with Azure Databricks and build scalable data solutions
• Develop and improve KPI and reporting solutions
• Consolidate and transform data from multiple sources
• Contribute to data modeling, data preparation, and optimization of existing data structures
• Support the development of new KPI dashboards
• Build and deploy solutions following CI/CD practices
• Collaborate closely with Data Analysts, Business Stakeholders, and Product Owners

What we’re looking for

• 3+ years of experience in Data Engineering
• Strong hands-on experience with Microsoft Azure
• Strong expertise in Databricks / Azure Databricks
• Experience building and operating Data Lakes
• Good understanding of CI/CD for data environments
• Experience working with enterprise-scale data platforms
• Strong English communication skills
• Independent, pragmatic, and result-oriented approach

Nice to have

• Experience with AWS data services
• Background in KPI, reporting, or analytics projects
• Experience in the energy or utilities sector

Fundable Senior Data Engineer

Build a data platform that turns information from many sources into reliable, up-to-date company intelligence.

About Fundable

Fundable is a startup data platform that helps investors and other teams discover and research companies. We bring together information on companies, investors, funding activity, and people to make that research easier.

Role overview

We’re looking for a senior data engineer to own Fundable’s data platform from architecture through production, reliably turning high volumes of data from provider APIs, webhooks, files, and web scraping into trusted records for our products.

Today, our enrichment and extraction workflows largely run serially inside a container. You’ll lead the move to a platform that supports concurrent processing, event-driven intake, and orchestrated batch workflows.

What you’ll own

• Multi-source intake: Build concurrent ingestion for provider APIs, webhooks, files, and scraping, with rate limits and failure handling.
• Batch and event-driven execution: Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect. Build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay of failed work.
• Raw-to-curated data: Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt. Establish medallion layers for raw, standardized, and curated data.
• Correctness through recovery: Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption. Make data traceable to its source and safe to reprocess.
• Data quality and delivery: Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures. Design and optimize PostgreSQL schemas, queries, and loading patterns so downstream products receive dependable data.
• Extraction and enrichment: Integrate provider enrichment and LLM-based structured extraction from text into the platform.

Must-have experience

• Experience building multi-source production data platforms from zero to one, from initial architecture and pipeline implementation through deployment and ongoing operation.
• Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and failure recovery. Airflow experience is preferred.
• Hands-on production experience with dbt and GCP.
• Experience building event-driven systems with messaging or task queues, including asynchronous processing and failure recovery.
• Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading. Experience with data contracts, raw and curated storage, retention policies, and automated quality checks.
• Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
• Hands-on web scraping experience with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrent scraping, rate limits, and failure handling.

Nice-to-have experience

• Experience working in startups or small engineering teams, with broad ownership and changing priorities.
• Entity resolution experience is especially valuable: matching company and people records across sources and reconciling duplicates into consistent entities.
• LLM-based structured extraction or company and people data enrichment.
• Familiarity with BigQuery and Dataform.

Location and hours

There is no required location or working-hours overlap.

We are seeking a skilled ML/AI Engineer to join our team and deliver cutting-edge ML/AI models in the ASR domain and legal insights more broadly. The core of this role is building, deploying, and serving ML models in production. 

A key part of this role is speech engineering — prior ASR/speech experience is not a must, but a willingness to learn and grow into this domain is essential. Experience with LLM-based solutions and agentic flows is an advantage, not a requirement. 

What you’ll do: 

• Build, deploy, and serve ML models in production — the core of the role — optimizing inference performance, latency, and efficient GPU utilization. 
• Support and extend our speech solutions, including 3rd-party ASR integrations and self-hosted ML models. 
• Evaluate and optimize key ASR/ML metrics such as WER, latency, F1 score, EER, and DER. 
• Secondary: Integrate LLMs and agentic flows into production solutions where they add value, including fine-tuning existing models. 

What you’ll bring: 

Core (required): 

• Solid background in machine/deep learning, with 4+ years of experience. 
• Strong Python skills and experience working in Linux environments. 
• Familiarity with deep learning libraries (e.g., PyTorch, TensorFlow) and training workflows. 
• Experience with MLOps pipelines and serving/deploying models in production. 
• Experience optimizing models for inference, including GPU acceleration. 
• Experience with Docker, gRPC, and serverless / micro-services architectures on cloud infrastructure (AWS/GCP/Azure). 
• Experience with real-time / streaming systems and low-latency processing (e.g. streaming pipelines, live sessions). 

Nice to have (secondary / optional): 

• Experience building and productizing agentic flows and AI-driven solutions (1+ year). 
• Familiarity with LLM / agent orchestration frameworks such as CrewAI, AWS Bedrock, Temporal, AgentCore, or LiteLLM. 
• Prior ASR / speech experience — not required; willingness to learn this domain is what matters most. 

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help investigate and fix production issues reported by real enterprise customers.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers
• Write performance-conscious code in Java and/or C++
• Investigate and fix production and customer-reported issues under the guidance of senior engineers, including fixes delivered across multiple supported release lines
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data
• Integrate with columnar formats, open table formats, and connectivity drivers
• Contribute to CI/CD and automated testing in Jenkins
• Deploy and validate changes on Kubernetes (GKE/EKS/AKS) across GCP, AWS, or Azure with Docker
• Debug issues across query planning, distributed execution, memory management, and I/O; collaborate with US-based teams on design and code reviews
•  

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 3+ years in backend / systems software engineering
• Strong proficiency in Java or C++ with solid OOP and software design fundamentals, including concurrency and asynchronous programming (comfort with both languages is especially valued)
• Strong SQL and understanding of relational and analytical data systems, including query execution concepts
• Hands-on experience with data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Experience with Jenkins pipelines and modern development workflows
• Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency)
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team

• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

   

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); exposure to distributed data platforms such as Spark, Snowflake, or Databricks
• Messaging systems: Kafka, NATS, or cloud pub/sub services
• Query planning, optimization, and execution internals
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS), multi-cloud exposure; Terraform

• Performance profiling of latency-sensitive systems

   

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We are looking for a Senior Data Engineer to join an experienced technical team working on modern data platforms and large-scale data processing.

The role combines data architecture, ETL, data warehousing, and big data / real-time pipelines, with solutions running both on-premises and in the cloud.
 

What you’ll do

• Design and implement data models for complex data environments
• Develop and maintain ETL/ELT processes
• Build and optimize big data and real-time data pipelines
• Work with data platforms across on-premises and cloud environments
• Design and implement solutions for data warehousing and data movement
• Work with relational databases and large datasets
• Optimize queries and data processing workflows
• Develop data engineering solutions using SQL and Python
• Collaborate with other engineers and technical stakeholders on data architecture and implementation
   

Requirements

• 3–5+ years of experience in Data Engineering
• Strong experience with database technologies and data architecture
• Hands-on experience with Oracle — especially relevant for this role
• Experience with data warehousing
• Experience with ETL and data movement technologies
• Excellent SQL skills
• Strong Python development skills
• Experience building big data and/or real-time data pipelines
• Understanding of both on-premises and cloud-based data environments
• Strong analytical and problem-solving skills
• Fluent English — required for daily communication
   

Education

• Master’s degree in Computer Science or a comparable technical qualification
   

What you’ll work with

• Oracle and other database technologies
• SQL
• Python
• ETL / ELT
• Data warehousing
• Big Data pipelines
• Real-time data processing
• On-premises and cloud infrastructure
   

What We Offer

• Paid vacation and sick leave
• Equipment to work comfortably, if required
• 50% coverage of professional development courses
• Regular salary reviews based on performance
• Competitive salary, depending on experience
• Always on-time payments — no delays
• A supportive team without unnecessary bureaucracy

We're looking for a Middle+ Data/Databricks Engineer to join our data team and take ownership of pipelines built on Databricks. You'll design and run production-grade ETL/ELT workloads, work with the lakehouse (Delta Lake) architecture, and partner closely with analysts, data scientists, and business stakeholders who rely on the data you deliver. "Middle+" here means roughly 3–5 years of hands-on data engineering experience: you can own a pipeline end to end with minimal supervision, make sound design decisions, and are starting to mentor more junior engineers — without yet carrying full architectural ownership of the platform.

Contract & Logistics

• Contract type: B2B (valid Czech IČO / trade license required).
• Location: Remote, candidate must be based in the Czech Republic; occasional visit to Prague office.
• Start date: ASAP
• English: Fluent 
• Czech: Native/Fluent 

What You'll Do

• Design, build, and maintain scalable ETL/ELT pipelines on Databricks using PySpark and SQL.
• Implement and evolve Delta Lake tables following a medallion (bronze/silver/gold) architecture.
• Develop batch and, where needed, streaming data processing jobs (Structured Streaming).
• Build and maintain CI/CD for Databricks notebooks and jobs (Databricks Asset Bundles, Repos, Git-based workflows).
• Orchestrate pipelines with Databricks Workflows, Azure Data Factory, Airflow, or similar tools.
• Monitor job performance and cluster costs; troubleshoot failures and optimize for reliability and spend.
• Implement data quality checks, testing, and monitoring for critical datasets.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into data models.
• Participate in code reviews, document technical decisions, and help shape engineering best practices.
• Provide guidance and informal mentorship to junior team members.

What We're Looking For

• 3+ years of hands-on experience with Apache Spark, ideally on Databricks specifically.
• Strong PySpark and SQL skills, including performance tuning of Spark jobs.
• Practical experience with Delta Lake and lakehouse architecture concepts.
• Solid grasp of data modeling (dimensional modeling, star schema) and ETL/ELT design patterns.
• Experience with at least one major cloud platform — Azure, AWS, or GCP (Azure Databricks is a plus).
• Familiarity with pipeline orchestration tools (Databricks Workflows, ADF, Airflow, or equivalent).
• Working knowledge of Git and CI/CD practices for data engineering.
• Understanding of data governance, access control, and security basics (Unity Catalog is a plus).
• Good written and spoken English; Czech or Slovak is a plus but not required.
• Comfortable working independently and communicating clearly in a remote, distributed team.

Nice to Have

• Databricks certification (Data Engineer Associate or Professional).
• Experience with streaming technologies such as Kafka or Azure Event Hubs.
• Experience with dbt for transformation and testing.
• Solid Python software-engineering practices — testing, packaging, typing.
• Infrastructure-as-code experience (Terraform).
• Exposure to MLOps or ML pipeline support.
• Experience in a regulated or large enterprise data environment.

What We Offer

• Fully remote work with flexible hours, based anywhere in the Czech Republic.
• Long-term B2B cooperation with a stable pipeline of work.
• A modern, actively evolving data stack rather than legacy maintenance work.
• Direct collaboration with senior engineers and architects — real input on design decisions.
• Budget for training, certifications, and conference attendance.
• A flat structure with fast decision-making and minimal bureaucracy.

Client

Our client is one of the leading Europe’s alternative asset managers that provide effective capital solutions to meet needs of wide range of corporate and real asset owners.

Project overview

The project focuses on building a cloud based data platform that centralizes, transforms, and delivers high quality data across multiple business domains. The environment supports advanced analytics, data governance, and scalable data processing using modern Azure and Snowflake technologies.

Position overview

We are looking for a Senior Data Engineer with strong expertise in Snowflake and Azure data technologies to contribute to the development and enhancement of a large scale enterprise data platform. You will work on designing, building, and optimizing modern data solutions that support analytics, reporting, and business decision making. This role is ideal for a data professional who enjoys working with cloud data platforms, complex data pipelines, and modern data warehousing architectures.
 

Technology stack

Snowflake, Snowflake SQL, Snowflake Stored Procedures, Snowflake UDFs, Snowpark, Python, Apache Airflow, Azure SQL Database, Microsoft SQL Server, T SQL, dbt, Airflow Cosmos, Snowflake CLI, Azure Cloud Services, Data Warehousing, ETL, ELT, Medallion Architecture, Layered Architecture, Dimensional Modeling, Git

Responsibilities

• Develop and maintain scalable data solutions on the Snowflake platform
• Design and implement Snowflake databases, schemas, tables, views, and materialized views
• Build, optimize, and support ETL and ELT data pipelines
• Create and maintain stored procedures and user defined functions using Python and SQL
• Design and develop data warehouse solutions using layered and Medallion architecture principles
• Optimize Snowflake and SQL Server performance through indexing, partitioning, query tuning, and workload optimization
• Develop complex SQL queries utilizing analytical and window functions
• Collaborate with architects, business stakeholders, and engineering teams to deliver high quality data solutions
• Support data quality validation, reconciliation, and testing activities
• Prepare and maintain technical documentation related to data solutions and architecture
• Contribute to continuous improvement initiatives and engineering best practices

Requirements

• Strong experience with Snowflake data platform engineering
• Advanced knowledge of Snowflake SQL
• Experience with Snowflake databases, schemas, tables, views, and materialized views
• Experience developing Snowflake stored procedures and UDFs using Python and SQL
• Strong experience with Apache Airflow
• Strong Python development skills
• Hands on experience with Azure SQL Database, Microsoft SQL Server, and T SQL
• Experience creating and optimizing tables, views, stored procedures, functions, indexes, and partitions
• Strong knowledge of SQL performance tuning and query optimization
• Experience developing ETL and ELT pipelines
• Experience building enterprise data warehouse solutions
• Knowledge of Layered Architecture and Medallion Architecture
• Experience with dimensional modeling
• Good verbal and written English communication skills

Nice to have

• Experience with dbt
• Experience with Airflow Cosmos
• Knowledge of Snowpark
• Experience working with Snowflake CLI
• Experience with Master Data Management design and integration
• Experience with QlikView or Qlik Sense
• Experience with data quality testing, validation, and reconciliation
• Experience creating technical documentation
• Experience within investment banking or asset management environments
• Experience using Claude AI within the Software Development Life Cycle

We are looking for a senior data engineer with deep Databricks experience to own the data platform behind one of our largest enterprise accounts and lead its migration from AWS to Databricks.

About the role

Data infrastructure is the foundation of everything we deliver. Our models, dashboards, and the decisions our clients make on top of them are only as reliable as the pipelines underneath, which is why we put the platform first.

You will join a small senior team working inside the client's environment. Through the rest of 2026 you will take ownership of the existing pipelines and the architecture around them. From 2027 you will lead the migration to Databricks: designing the target architecture, planning the cutover, and doing the implementation. This is a hands-on engineering role with real architectural authority. You decide how the platform gets built, and then you build it.

You will be the person the team turns to on anything Databricks, working alongside senior machine learning engineers and data scientists who depend on what you deliver.

What you will do:

• Design and build the target Databricks architecture, and plan the migration from the current AWS stack
• Build and maintain production data pipelines: ingestion, transformation, and orchestration
• Own data quality, including the checks and alerts that catch problems before the client does
• Design the data models the analytics and machine learning work sits on
• Support the pipelines you build: monitoring, alerting, and the on-call reality of production data
• Work directly with the client's engineers, and explain technical decisions to people who are not engineers

Requirements:

• Five or more years in data engineering or software development
• At least three years of production Databricks work
• Deep experience with Spark, Delta Lake, and Unity Catalog
• Has planned and executed at least one migration onto Databricks from another platform, and can walk through the challenges
• Expert Python: clean modular code, comfortable with optimization and profiling
• Advanced SQL and relational database work: raw SQL, data modeling, scripted migrations
• Systems design: how storage and compute fit together, end to end pipelines, containerized applications
• Cloud operations on AWS or equivalent: networking, identity and access management, monitoring and alerting, and the domain knowledge for why things break
• Comfortable working inside a client's environment, with their data and their constraints
• Strong written English, and spoken English good enough to present your work
• Available for some overlap with United States working hours

Nice to have:

• Databricks certification
• Deep AWS experience specifically, since that is the platform we are migrating from
• dbt, Airflow, or a similar transformation and orchestration framework
• Machine learning pipeline experience, since models run on top of what you build
• Background in utilities, energy, telecoms, or another regulated industry
• Experience being the senior engineer a client relies on directly

You are:

• Careful with data quality, and you ask when something looks wrong instead of guessing
• A self starter who can turn a general requirement into a specific estimate and a deliverable
• Someone who documents. We write things down, and every number we deliver carries its source
• Comfortable in a small remote team where nobody is checking your hours

About Bennett Data Science

We have spent ten years building predictive analytics and data science for enterprise clients, from some of the largest companies in the world to startups in Silicon Valley. Our engineers sit inside the client's systems and build critical systems. The work ships to production and lands at the bottom line.

What we offer:

• Competitive compensation, reviewed as you grow
• Work from anywhere, with a flexible schedule
• A small senior team, so your work is visible and your decisions are important
• Direct exposure to enterprise clients and the problems they actually have
• Mentorship from senior staff, and the space to grow and mentor others