Top Data Engineer Jobs in Warsaw

Design, build, and maintain Azure Data Factory pipelines and ETL/ELT processes; monitor and optimize ADF; develop Python components (Azure Functions, API integrations); support Azure SQL/Synapse warehousing and occasional Power BI reporting; contribute to Azure DevOps CI/CD and collaborate with Product Owner and Architect to deliver scalable data platform improvements.

The Data Engineer will design and maintain data pipelines, optimize performance, enforce governance, and collaborate with AI teams for innovative solutions.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain full-stack data and AI solutions: scalable data pipelines, GenAI features (RAG, embeddings, LLM assistants), backend APIs/microservices, and frontend dashboards. Deploy and monitor components on cloud platforms, apply engineering best practices, and collaborate with stakeholders to deliver production-ready automation and analytics.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Lead a data engineering team, develop AI self-service portal and data pipelines, interface with internal teams, and ensure software engineering best practices.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Analyze user behavior on websites and apps, focusing on KPIs and data visualization. Build dashboards, run A/B tests, and communicate findings to stakeholders.

Owner of all PostgreSQL databases underpinning a multi-site telecom platform. Responsibilities include DB installation, HA, backup/restore, performance tuning, large-scale bulk loads/migrations, multi-datacenter replication, data governance (retention, masking, archival), monitoring (Prometheus/Grafana), automation scripting, and cross-platform integration with network-element databases.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Design and maintain data pipelines, ensure data reliability, structure data systems, and manage software development processes while collaborating in a team.

Develop software for risk data pipelines, collaborating with trading teams to manage trade data, improve capital efficiency, and automate operations.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design, build, and maintain scalable Databricks ETL/ELT pipelines using Python and SQL. Optimize cloud data workflows, support data models and integrations across Azure/AWS/GCP, monitor and troubleshoot pipelines, and collaborate with analysts and data scientists to deliver clean data for AI and analytics.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Lead design and build of cloud-native, production-grade data platforms and real-time pipelines. Own architecture, implement scalable ingestion, storage, processing, and access layers, ensure security and governance, optimize performance, mentor engineers, and consult directly with clients to deliver data products and solutions.