Top Data Engineer Jobs in Krakow

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

Build, test, and maintain production data pipelines to process AST outputs, knowledge graphs, and vector embeddings; ingest and normalize code dependency graphs into Neo4j and Qdrant; transform source-code metadata into Markdown/JSON assets; monitor pipelines, debug batch errors, and collaborate with senior engineers to optimize retrieval and efficiency.

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

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).

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

The Data Engineer will develop, maintain, and optimize data pipelines using Apache Airflow, manage database performance, and support Elasticsearch integration. Responsibilities include building ETL scripts, handling Unix/Linux operations, and implementing CI/CD pipelines in GitLab.

Own the end-to-end architecture and implementation of a scalable data platform processing real-time IoT telemetry and predictive analytics. Build Kafka-based streaming and batch pipelines, Kubernetes workloads, AWS infrastructure, relational data models, integrations, and CI/CD systems. Lead performance, reliability, observability, privacy, and compliance efforts in regulated healthcare environments, while applying AI-assisted engineering practices and participating in 24/7 on-call support.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

The Technical Support Engineer II will troubleshoot and resolve customer issues related to digital communications systems, while supporting internal/external communication and management of customer expectations.

Design and implement decision-making logic for a price optimizer: translate pricing rules into mathematical models and production-ready Python code, refine simulation and optimization algorithms, validate model behavior using data, and collaborate with architects and data engineers for pipeline integration.

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.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional 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.

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.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings; ingest and normalize code dependency graphs into Neo4j and Qdrant; transform source metadata into structured Markdown/JSON assets; monitor pipeline execution and resolve batch errors; collaborate with senior engineers and AI/ML 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, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Leads the design and development of enterprise data warehouses, lakes, and governed data platforms. Builds scalable batch and real-time ETL/ELT pipelines using Databricks, SQL, Python, and PySpark; drives legacy migration and enterprise data integration. Enables analytics, automation, and AI use cases while establishing engineering standards, mentoring technical teams, and partnering with stakeholders on strategic data transformation initiatives.

Design and implement responsive React+TypeScript frontends for GenAI products. Build conversational UIs, streaming interfaces, agent dashboards, and data visualizations. Ensure UX quality, accessibility, frontend security, and integrate with backend APIs, LLM services, and streaming endpoints while collaborating with cross-functional teams.

This role involves designing data architecture, building data transformation pipelines, and maintaining Power BI and Azure solutions. You will ensure data quality and support business intelligence reporting.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Build and maintain high-performance market data infrastructure in Rust to ingest, process, store, and serve real-time pricing feeds. Solve latency, data quality, and cross-exchange consistency problems, contribute trading/market-data expertise to engineering decisions, communicate with a distributed team, and potentially lead a small team over time.

Build a greenfield data platform and unified Lakehouse integrating high-volume payment, marketing, and operational data across AWS and GCP. Design ingestion pipelines, create dbt medallion-layer transformations in BigQuery, and own Airflow orchestration, lineage, backfills, retries, alerting, infrastructure as code, and cost controls. The role requires autonomous ownership in a fully remote, asynchronous environment, with data mesh and Starburst Galaxy experience as preferred qualifications.

Design, build, and maintain scalable data pipelines for BigQuery migrations. Develop ingestion and transformation frameworks, orchestrate and optimize workloads, ensure data quality, and deliver production-grade data products for reporting and analytics. Collaborate with analytics engineers, architects, and stakeholders on platform migration and sustainability. Required capabilities include Python, SQL, BigQuery, ETL/ELT, data modeling, orchestration, Git, CI/CD, cloud-native architectures, testing, monitoring, and data validation.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.