Top Data Engineer Jobs in Poland

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.

As a Senior Analytics Engineer, you'll design and deliver scalable data products, ensuring they meet business needs through collaboration and adherence to standards.

The Senior Data Engineer will design and maintain data systems, build data pipelines, optimize ETL processes, and ensure data quality, collaborating with various teams to facilitate data-driven decision making.

Design, implement, and optimize data ingestion, processing, storage, and sharing systems. Build scalable ETL/ELT pipelines, ensure data quality and security, integrate systems, implement monitoring, and collaborate with analysts and stakeholders. Troubleshoot, perform code reviews, and gain banking-domain expertise.

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.

As a Data Engineer, you will lead data initiatives, build scalable ETL pipelines, ensure data quality, and collaborate with teams to drive data-driven decisions.

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 Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

Design, build, and operate scalable ELT/ETL pipelines; develop and maintain Airflow orchestration; build and optimize Snowflake/Databricks data models; improve data quality, testing, monitoring, and lineage; troubleshoot production incidents; collaborate with analytics, data scientists and engineers; contribute to engineering standards and use AI coding tools to accelerate development.

Design and implement scalable batch and streaming data pipelines on Databricks and cloud platforms; build data transformations, CI/CD and MLOps practices; collaborate with data scientists; create data governance, monitoring, and Power BI visualizations; translate business requirements into performant technical solutions.

Design, build, and maintain scalable data pipelines and platforms for batch and streaming data using Spark, Airflow, Iceberg, and cloud services. Optimize ingestion, processing, and storage, collaborate with stakeholders, monitor platform performance, and write high-quality Python/Java/Scala code to support enterprise reporting and analytics.

Design, build, and own scalable data products on Snowflake/AWS using dbt and Data Vault. Implement ETL/ELT pipelines, CI/CD releases, data quality/observability (Monte Carlo), and governance (Collibra, Immuta). Partner with product and architecture teams, support release automation, and establish engineering best practices within a Data Mesh framework.

Design, build, and maintain scalable Azure-based data platforms using Microsoft Fabric, Databricks, Data Lake and ETL/ELT pipelines. Create data models, improve performance and cost efficiency, troubleshoot issues, collaborate with business stakeholders, mentor junior engineers, and stay current with Azure best practices.

Looking for a Data Engineer to implement cloud-native data platforms, engineer scalable pipelines, and manage cloud resources. Ideal candidates have strong Python and SQL skills, and experience with tools like Spark and Terraform. Familiarity with MLOps is a plus.

The Data Engineer will optimize backend services for a SaaS platform, develop data pipelines using SQL and Python, and collaborate in an Agile environment to implement cloud solutions on Microsoft Azure.

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 Senior Software Engineer will lead engineering teams, mentor junior engineers, and ensure system reliability and performance while developing backend services.

Build and maintain scalable data pipelines and warehousing to support ML and LLMs. Assist in model development, deployment, data transformation, quality, and performance optimisation, collaborate with data scientists, document workflows, and evaluate new AI technologies.

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 maintain scalable ETL/data pipelines and foundational datasets for product, marketing, and finance. Ensure data quality through validation and monitoring, optimize infrastructure for scale, and collaborate with analysts and engineers. Integrate AI tools to improve workflows and automation.

Design, build, and maintain scalable data pipelines; integrate diverse data sources into warehouses or lakes; collaborate with data scientists and engineers to ensure quality and availability; optimize schemas, models, and performance; implement governance, security, and compliance; monitor reliability and document systems.

Build, maintain, and optimize ETL pipelines and analytical data warehouse infrastructure. Deploy, monitor, and operate machine learning models in production, automate model lifecycle and data processing, collaborate with Data Science/BI/Engineering/Risk/IT, troubleshoot performance issues, and maintain technical documentation.

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 Senior Data Engineer will design, build, and maintain data systems, pipelines, and infrastructure for data processing and analysis, collaborating with stakeholders and ensuring data quality.