Top Data Engineer Jobs in Krakow

Design and build large-scale analytics pipelines and data models (Spark/Databricks). Define data integrations, lineage, and quality frameworks. Collaborate with product, engineering, and data science to shape long-term data platform architecture, create dashboards and visualizations, and optimize pipelines. Occasional on-call for operational issues.

Lead the technical architecture and long-term strategy for GIS data pipelines and platforms. Drive cross-team GIS engineering initiatives, select and adopt tools (ArcGIS, FME, cloud GIS), enforce data engineering standards, and ensure scalable, reliable geospatial products. Act as technical reviewer, mentor engineers, collaborate with solution architects and customers, and oversee QA/QC, performance optimization, and multi-format data harmonization.

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.

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

The AI Data Engineer will design and build AI data pipelines, ensuring reliability and quality for predictive intelligence in the Large Market Model.

Design, build, and maintain AWS-based lakehouse solutions and medallion architecture using Iceberg and Glue. Implement CDC and streaming ingestion with Debezium and Kafka. Develop PySpark batch/distributed jobs on EMR/Glue, orchestrate workflows with Airflow, ensure data quality/governance, and collaborate on infrastructure and IaC practices to deliver reliable business-ready datasets.

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.

Design, develop, validate, deploy, and improve machine learning models; analyze large datasets; apply statistical methods and experimentation; collaborate with data engineers for data preparation; present insights to stakeholders and mentor junior team members.

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.

Design and build scalable Snowflake-based data solutions, ETL/ELT pipelines, and reusable data models. Optimize data extraction from Robotics and ERP sources, enable data-as-product with governance and access controls, and consult on architecture and tooling to support analytics, ML, and IoT use cases.

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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

Build and maintain Python-based data pipelines to extract, clean, transform, and load CRM, Back Office, and Jira financial data. Run monthly invoicing processes, generate client reports and Tableau dashboards, troubleshoot API and data issues, and collaborate with Sales, Account Management, Billing, Legal, and Integration teams to ensure data accuracy and insights.

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.

As a Data Engineer, you will build and maintain ELT data pipelines, develop dbt models, and work with cross-functional teams to ensure data quality and reliability.

As a Data Engineer II, you will design scalable data pipelines in AWS, develop SQL transformations, and enhance MLOps workflows, focusing on analytics and machine learning initiatives.

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.

Design, build, and maintain scalable data pipelines on GCP; optimize data models; ensure data quality; support legacy data migration; collaborate with teams; implement monitoring; contribute to data governance best practices.

The role involves designing and scaling MLOps infrastructure on AWS for autonomous driving systems, requiring expertise in machine learning and cloud architecture.

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.

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.

Design, develop, and maintain scalable cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and optimize SQL Server/SSIS-based data warehouses, ensure data quality and performance for large datasets, and collaborate with BI teams to support analytics and reporting.

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.

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.