Top Data Engineer Jobs in Poland

Lead migration from SAS/Exadata to a Microsoft Fabric data platform: build and maintain data pipelines, migrate/validate reports and data products, implement data quality, metadata/catalog processes, optimize performance, provide testing, documentation and post-go-live support, and enable knowledge transfer.

Monitor and maintain production ETL pipelines, handle incident investigation and resolution, improve pipeline stability, manage operational tickets and documentation, and monitor KPI metrics and dataset availability.

Lead migration and modernization of legacy analytics to Microsoft Fabric: design lakehouse architectures, build ingestion pipelines, implement governance, CI/CD and data quality, and deliver enterprise-scale data migrations while liaising with stakeholders.

Operate and monitor ETL/data pipelines, provide incident support and on-call coverage, manage tickets and KPIs, document processes, drive continuous improvements, and ensure reliable, accurate report delivery to clients.

Build and optimize Python-based ETL/ELT pipelines to migrate on-prem MS SQL Server and historical Parquet data into Databricks. Ensure data quality, schema mapping, transformations, and governance. Tune performance using Databricks/Delta Lake, collaborate with AI teams and infra, and maintain documentation and secure data transfer processes.

Support building and maintaining data pipelines, perform ETL in Azure/Databricks, write SQL and simple Python scripts, work with large datasets, monitor data quality, troubleshoot basic issues, and collaborate on data analysis.

Build and operate real‑time streaming data pipelines for ML inference: migrate batch inference to streaming, design Kafka topics and partitioning, implement checkpointing and delivery guarantees, package and version models, monitor and tune pipeline performance, and collaborate with Data Scientists on CI/CD and model monitoring.

Design, implement, and operate GenAI-based mail processing: integrate MCP servers and OCR pipelines, enrich context for LLM prompts, scale and tune Python microservices on Kubernetes, harden LLM gateway, improve PostgreSQL connection handling, and add observability and runbooks.

Design, build, and optimize data pipelines and frameworks to ingest data into a Data Lake from sources like Kafka, MQ, SFTP and APIs. Implement testing, ensure data quality, governance, security, and collaborate with analysts and data scientists while researching performance improvements.

Design, build and maintain data pipelines and Big Data processing using Databricks and Spark on Azure. Develop SQL and Python code for data processing and storage, apply Big Data standards and tools, and collaborate with teams on data engineering tasks. Hybrid role requiring periodic office presence in select Polish cities.

Design and deliver end-to-end data architecture on Microsoft platforms. Define capacity, performance, security, governance, integration, and data modeling standards. Support implementation of lakehouse and data warehouse solutions, optimize platform performance, and collaborate with stakeholders to build a scalable data ecosystem.

Build and optimize scalable Big Data solutions for a real-time AdTech risk assessment platform. Responsibilities include Snowflake optimization, SQL and data pipeline performance improvements, cost efficiency initiatives, Java-based backend development, architecture enhancements, and high-load platform engineering. The role also supports new features, reliable services, and collaboration with distributed engineering teams.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings. Ingest and normalize code dependency graphs into Neo4j and vector indexes in Qdrant, transform source code into structured Markdown/JSON, monitor pipeline runs, debug errors, and collaborate with senior engineers to optimize retrieval and pipeline efficiency.

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, develop, and maintain scalable data solutions and pipelines using Databricks, Apache Spark, and Azure. Build and optimize data processing workflows, ensure data quality, performance, and security, and collaborate with analysts, architects, and stakeholders to deliver business value.

The AI Data Engineer will design and implement workflows for automating data reporting, using AI to enhance the telemedicine platform's analytics.

Design, build and maintain Snowflake-based data warehouses and dbt models; write advanced SQL; automate pipelines with CI/CD and container concepts; use Git workflows; perform data processing with Python. Provide German-language support (minimum B2).

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Design, develop, and optimize scalable data pipelines on Snowflake. Collaborate with cross-functional teams, implement data modelling and performance best practices, ensure data quality/security, automate workflows, integrate diverse data sources, and monitor/troubleshoot pipelines.

The AI/NLP/Data Engineer will develop ML and AI models, enhance NLP systems, and build supporting infrastructure while collaborating with teams on deployment and documentation.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design, build, and maintain scalable batch and real-time data pipelines, ELT workflows, cloud data infrastructure, orchestration, and streaming solutions. Ensure production reliability, performance, security, governance, monitoring, and data quality while collaborating with technical and business stakeholders. Participate in on-call support and continuous improvement initiatives.

Build scalable pipelines to ingest and process high-volume unstructured insurance documents, including PDFs, scans, emails, Word, Excel, and PowerPoint files. Responsibilities include OCR integration, document parsing, text normalization, semantic chunking, metadata extraction, enterprise connectors, retrieval-ready data preparation for RAG systems, validation, monitoring, logging, testing, and secure cloud deployment using software engineering best practices.