Top Data Engineer Jobs in Poland

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and maintain backend services, REST APIs, databases, and near-real-time and batch data pipelines. Develop a query engine and scalable insights pipelines, collaborate with product and engineering, improve legacy code, and ensure production monitoring, alerting, and tooling for support.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

Design, build, and own scalable data products on Snowflake/AWS using dbt and Data Vault. Implement ETL/ELT pipelines, CI/CD releases, data quality/observability (Monte Carlo), and governance (Collibra, Immuta). Partner with product and architecture teams, support release automation, and establish engineering best practices within a Data Mesh framework.

Design and implement data models, build ETL workflows and Data Marts, and develop automated reporting dashboards for credit risk. Collaborate with data architects, prepare documentation, and translate business requirements into scalable reporting solutions.

Design and build a scalable cloud data platform on Google Cloud, create reliable batch and streaming ETL/ELT pipelines, enforce data quality and observability, automate IaC/CI-CD, and support analytics, ML, and product teams with high-quality data.

Design and implement enterprise knowledge graph solutions, build semantic models/ontologies, optimize graph data models and queries, integrate graphs with Data & AI platforms (RAG/Agentic AI), and collaborate with cross-functional teams and clients on graph and AI initiatives.

Build and operate a scalable data platform enabling domain teams to self-serve. Implement Data Reliability Engineering (SLOs, data quality), optimize Snowflake costs, migrate legacy flows, and automate infrastructure with Terraform and AWS Fargate. Partner with warehouse, finance, and business teams to ensure production-grade, secure, and cost-efficient data systems.

Design, build, and maintain data pipelines for data science, collaborate with teams, troubleshoot data issues, and ensure data quality.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

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.

The Senior Data Science Engineer will architect software solutions, drive AI research, design AI systems, and mentor junior engineers while focusing on innovative solutions in the travel domain.

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.

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 build data architectures and pipelines, optimize data management, collaborate with AI teams, and implement best engineering practices.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design, build, and operate reliable, scalable distributed data infrastructure and connectors. Develop RESTful services, address multi-threading and fault-tolerance, collaborate across teams, and contribute to Netflix OSS and platform tooling.

Lead migration of a large-scale analytics platform from BigQuery to Databricks. Design and implement Lakehouse architectures, build and optimize PySpark data pipelines, implement medallion layers and Unity Catalog governance, troubleshoot complex SQL/Spark workloads, contribute to architecture and CI/CD, and support production deployments for retail analytics workloads.

Design, build, and maintain scalable ETL/ELT pipelines that ingest and normalize ad-tech and ancillary data. Build and optimize data warehouses/lakes, implement data quality monitoring, support BI/analytics teams, and ensure GDPR/CCPA compliance for ad-tech analytics at scale.

Build and scale data pipelines and warehouses for an AdTech BI platform. Ingest and normalize SSP/DSP/GAM log-level and API data, transform raw events into analytics-ready datasets, implement data quality monitoring, support BI teams, and ensure privacy compliance for large-scale, real-time ad analytics.

Build and scale ad‑tech data infrastructure and ETL/ELT pipelines ingesting SSP/DSP/GAM and ancillary data, maintain data warehouses/lakes, implement data quality and privacy controls, and support BI/analytics for real‑time campaign and auction analytics.

Design, build, and maintain scalable data pipelines and warehouses for ad-tech analytics. Ingest and normalize SSP, DSP, and GAM data; develop ETL/ELT workflows, data models, quality monitoring, and privacy-compliant processing to power BI dashboards and real-time analytics.