Top Data Engineer Jobs in Krakow

Lead architecture and strategy for GIS data pipelines and platforms. Support architects and engineers on spatial data modeling, formats, projections, and validations. Lead cross-team initiatives, select tools (ArcGIS, FME, cloud GIS), enforce data engineering standards, review complex designs, mentor engineers, manage SMEs, and ensure scalable, reliable, compliant geospatial data products.

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, 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, 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.

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, 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.

Develop and maintain data-centric AI/ML software for automotive ADAS and UX: build predictive models, support data analysis and visualization, monitor ML training data quality, research big-data technologies, program in Python/C++, optimize solutions, run trials, write technical documentation, and collaborate in project teams while following company quality systems.

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

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.

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, 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, 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 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.

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

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 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.

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

Design and build reusable Databricks Lakehouse PySpark pipelines, implement data models (fact/dimension, SCD Type 2), enable incremental/idempotent loads, optimize performance, implement event-driven integrations, collaborate on Azure cloud resources, and improve data platform architecture.

Design and implement scalable cloud data infrastructure, ETL pipelines, data models, and warehouses. Develop data processing solutions in Python and SQL, integrate legacy and modern systems, prepare data for AI use cases, gather requirements, produce technical specifications, scope releases, manage code reviews, and liaise with customers.

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 analytics-ready datasets and ETL for cybersecurity reporting; develop scripts and dashboards for tools like CrowdStrike, Prisma/Cortex, and MS InTune; create Power BI on-demand and static reports; translate security objectives into measurable metrics and present to technology leaders.

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.