Top Data Engineer Jobs in Poland

We are seeking a GCP Data Engineer to build ETL/ELT pipelines, maintain dbt models, optimize BigQuery performance, and support data access in an international, remote team.

As a Data Engineer, you will implement and manage Profisee Master Data Management solutions, ensuring high-quality master data through integration and collaboration with stakeholders.

Design, build, and maintain scalable data pipelines on Databricks for analytics and AI/ML. Implement ETL/ELT, medallion-style models, CI/CD and orchestration, ensure data quality, governance, security, performance, and troubleshoot production issues while collaborating with cross-functional teams.

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, 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.

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 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.

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.

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

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

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 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.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

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 Full Stack Data Engineer will design and build scalable data pipelines, optimize Snowflake architecture, develop dashboards and analytics solutions, and collaborate with cross-functional teams to ensure data reliability and meet business requirements.

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.

Design, build, and maintain scalable Azure-based data pipelines and solutions using Fabric/Data Factory and Python. Create and manage Power BI datasets, optimize workflows, and collaborate with business and engineering teams to deliver analytics and insights.

Build, operate, and maintain batch and real-time data pipelines using Azure, Databricks, Python and streaming tech (Kafka, Spark, Flink). Ingest and validate time-series and event data, ensure data quality, lineage and monitoring, and collaborate with Data Scientists, ML Engineers and platform teams to enable forecasting and real-time analytics.

Design, build, and maintain scalable data pipelines and Lakehouse architectures on Azure Databricks. Develop PySpark and Spark SQL transformations, orchestrate ETL/ELT with Azure Data Factory, integrate diverse data sources, optimize performance, ensure data quality, support SQL Server/SSIS platforms, and collaborate with BI teams for reporting and analytics.

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.