Top Data Engineer Jobs in Krakow

Lead design and delivery of the end-to-end data platform for an AI-powered SaaS product. Own Snowflake data architecture, build ELT/ETL pipelines, develop enterprise Power BI reports, establish data modeling, quality, and observability standards, mentor data engineers, and collaborate with product, engineering, and finance stakeholders to deliver trusted analytics and BI.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Design, build, and maintain large-scale data processing systems and pipelines (Spark, BigQuery, dbt/Dataform). Improve scalability, performance, and reliability; own projects end-to-end; extend and operate Airflow; work with cloud (GCP/AWS), Kubernetes, and CI/CD practices.

Lead a team to design, build and maintain scalable Azure data platforms and pipelines (ADF, Databricks, Synapse). Own data modeling, architecture, performance optimization, CI/CD, automation, troubleshooting, and stakeholder collaboration to support analytics and transformation initiatives.

Design, build, and operate GitOps-driven data and AI platforms and pipelines (ETL/ELT, feature stores, batch/real-time inference). Enable GenAI/RAG (embeddings, vector ingestion), implement IaC and CI/CD, enforce data quality, governance, observability and MLOps, and support Azure/AWS-based production deployments while collaborating with security, product, and operations teams.

Design, build, and maintain ELT/ETL pipelines into Snowflake, model data for analytics, deliver Power BI reports, embed data quality and observability, and use AI tools to accelerate development while collaborating across engineering, product, and finance.

Design, build, and maintain ELT/ETL pipelines into Snowflake, produce governed Power BI analytics, implement data quality/observability, optimize data models for performance and cost, and use AI tools to accelerate development while collaborating across product and finance teams.

Build and operate production data pipelines covering ingestion, transformation, orchestration, monitoring, and data quality. Develop dbt models, author and debug Airflow DAGs, provision AWS infrastructure with infrastructure-as-code, and maintain CI/CD using GitHub Actions. Support AI-ready data initiatives and Redshift access, while owning requirements through deployment and incident resolution. The role requires strong Python, SQL, AWS, analytical data modeling, autonomy, and stakeholder collaboration.

Design, implement, and optimize ETL/data pipelines, data quality metrics, APIs, and enrichment for AI/ML platforms; research big-data technologies; run trials, write technical documentation, and collaborate in project teams.

Build and operate production-grade batch and real-time data pipelines on GCP (BigQuery, Airflow, Kafka), ensure data quality via contracts and observability, develop connectors and integrations, support ML feature pipelines, and mentor junior engineers while contributing to engineering standards and CI/CD practices.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Build compliant, high-quality real-time data pipelines for CDR, network telemetry, and customer data. Enable Kafka and Flink streaming for AI use cases, implement data quality, lineage, and governance controls, manage feature engineering pipelines for machine learning models, and ensure compliance with data privacy regulations. Success is measured through real-time readiness, data quality, and pipeline reliability.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate data from multiple sources, optimize large-scale processing, support SQL Server/SSIS environments, and collaborate with BI teams to enable analytics and Power BI reporting while evolving the platform toward scalable cloud architectures.

Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.

Design, build, maintain, and troubleshoot data pipelines and processing systems for production and analytics. Improve data quality, drive optimization and tooling, and collaborate with engineers, ML experts, and stakeholders in agile teams while engaging with clients.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

Design, build, and maintain scalable ETL/ELT pipelines and dbt models to ingest, transform, enrich, and ensure quality of firmographic data. Support entity resolution/matching, monitor pipelines with alerts and metrics, collaborate with product and data teams, and document standards for maintainability.

Build and maintain Python-based data pipelines on Databricks, collaborate with product and domain experts, debug Tier-2 system issues, create documentation and onboarding processes, write integration tests, manage code in GitLab, and improve data quality and self-service tooling.

Design and build reusable ETL/ELT pipelines and integrations for Autodesk platforms, prototype AI-enabled workflows (LLMs, agents, MCP), produce demos and feasibility studies, validate integrations, document reusable implementation patterns, and collaborate with product, security, and consulting teams to operationalize customer-facing data solutions.

Designs, builds, and supports GCP-based data ecosystems, pipelines, storage architectures, and data models. The role gathers requirements, works directly with clients, optimizes queries and infrastructure, troubleshoots high-impact data issues, monitors system performance, and implements data integration and transformation solutions. It also involves mentoring junior data engineers and collaborating with cross-functional teams and distributed stakeholders.

Design, own, scale, and iterate data processing pipelines for Mapbox Roads. Implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate in a cross-functional team to build navigation and high-definition mapping features.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory / Microsoft Fabric to integrate APIs, databases, and files into Azure Data Lake and Azure SQL. Ensure data quality, validation, logging, monitoring, CI/CD, performance tuning, and production troubleshooting to support enterprise analytics and Power BI reporting.