Top Data Engineer Jobs in Poland

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 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.

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 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.

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.

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 cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize performance for large datasets, support SQL Server/SSIS systems, and collaborate with BI teams (Power BI) to enable analytics and reporting.

Design, build, and maintain scalable data pipelines and Lakehouse architectures on Azure Databricks. Develop ETL/ELT using PySpark, Spark SQL, Delta Lake, and Azure Data Factory; integrate SQL Server sources, ensure data quality and performance, and support BI/reporting (Power BI). Collaborate with teams to evolve cloud data platforms.

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

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple sources, optimize large-scale data processing, support SQL Server-based warehouses and collaborate with BI teams for analytics and reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark/Spark SQL, Delta Lake, and Azure Data Factory. Integrate multiple sources, support SQL Server/SSIS platforms, ensure data quality and performance, and collaborate with BI teams for analytics and reporting.

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 maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale Spark transformations and SQL Server ETL/SSIS processes, ensure data quality and performance, and support BI teams (Power BI) for analytics and reporting.

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.

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.

Design, build, and maintain batch engines and data marts for credit risk (PD, EAD, LGD). Implement regulatory-compliant pipelines, liaise with analysts and modelers, manage incidents and priorities, and contribute to digitalization and tooling for the Risk domain.

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.

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 transformations using Microsoft Fabric, Spark/PySpark, Python and SQL. Implement Lakehouse/OneLake and Fabric Data Warehouse solutions, integrate multiple data sources, support CI/CD with Azure DevOps, ensure data quality, performance and security, and create semantic models and Power BI reports while collaborating with business and technical stakeholders.

Build and maintain scalable data pipelines and ingestion workflows using Microsoft Fabric and Azure. Develop transformations with Spark/PySpark, Python and SQL; design OneLake/Lakehouse and data warehouse solutions; create semantic models and Power BI reports; support CI/CD and deployments; ensure data quality, performance, security, and stakeholder collaboration.

Design, build and maintain scalable data pipelines and transformations using Microsoft Fabric, Azure and Spark. Create semantic models and Power BI reports, support CI/CD, ensure data quality, security and collaborate with stakeholders to deliver data platform solutions.

Design and deliver end-to-end Microsoft-based data architecture solutions (Fabric, ADF, ADLS, Synapse). Define capacity, performance, security, governance, integration architecture, and data modeling standards.