Top Data Engineer Jobs in Poland

Design, develop, and maintain scalable data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory; integrate and model data from multiple sources, optimize performance for large datasets, support SQL Server/SSIS platforms, and collaborate with BI teams to enable analytics and reporting.

Design, develop, 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 ETL/ELT transformations, support SQL Server/SSIS platforms, ensure data quality and performance, and collaborate with BI teams to enable analytics and reporting.

Design, develop, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and transform data from multiple sources, support SQL Server/SSIS environments, optimize performance for large datasets, and collaborate with BI teams for Power BI reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures on Azure Databricks using PySpark, Spark SQL, Delta Lake and Azure Data Factory. Integrate sources into analytical/data warehouse layers (SQL Server/SSIS), ensure data quality, scalability and performance, and support BI teams (Power BI). Contribute to modern cloud data platform design and optimization.

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 processing, support SQL Server/SSIS platforms, and collaborate with BI teams for analytics and reporting.

Join Guesty as a Data Engineer to build and maintain scalable data systems, optimize ETL processes, and collaborate across teams for data-driven operations.

As a Data Engineer, you will develop and support big data processing pipelines, build cloud-based infrastructure, and provide visualization solutions for data assets.

Seeking a Senior Associate, Full-Stack Engineer to develop scalable database solutions, optimize data models, and collaborate cross-functionally. Responsibilities include data management, performance tuning, and implementing ETL processes.

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

Design, build, and operate a unified data platform and domain pipelines. Implement batch and real-time ingestion, data quality, observability, metadata, lineage, access controls, and optimize datasets for analytics and ML. Collaborate with Data Scientists and ML Engineers and contribute to platform roadmap and AI readiness.

Lead foundational data-engineering work to validate and re-engineer pipelines for an anonymized, centralized credit data lake. Harmonize schemas across entities, build dbt models and tests, implement data-quality suites (Great Expectations), entity resolution, anonymization controls, optimize Spark/Glue jobs, orchestrate pipelines (Airflow/Step Functions), and produce documented, feature-ready datasets and runbooks for a regulated UK/Ireland lending environment.

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 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.

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

Design, implement and govern HR data pipelines and processes to ensure group-wide People & Culture data consistency. Support system migration and data alignment, analyze large datasets for inconsistencies, document mappings and playbooks, and communicate findings to non-technical HR stakeholders. Work closely with Group HR Controller, country HR Heads, project teams and technical teams to enable accurate HR reporting and compliance with GDPR.

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 Leading Data Engineer, you will manage a team delivering customer data solutions using Snowflake, ensure platform reliability and compliance, and drive engineering best practices.

The AI Data Engineer will design and implement workflows for automating data reporting, using AI to enhance the telemedicine platform's analytics.

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

The AI Data Engineer will develop and maintain AI data pipelines, ensure system reliability, and innovate within AI technologies.

The AI Data Engineer will develop and maintain data pipelines for the Large Market Model, focusing on feature engineering, system reliability, and innovation in AI solutions.

Build and maintain GCP-based data warehouse and foundational data pipelines to enable analytics, e-commerce apps, and ML. Develop dataflows in SQL, software in Python/Java, implement CI/CD, infrastructure as code (Terraform), dbt transformations, and ensure scalable, secure data infrastructure across the Data Mesh.

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.

Lead and own the data ecosystem: architect, design and maintain scalable data pipelines (Azure Data Factory), build and optimize data models in SQL Server, develop Python/PySpark automation, implement Databricks/Spark workflows, resolve pipeline issues, improve data quality and resilience, and mentor junior engineers while aligning data solutions to business needs.

As a Data Engineer, you will lead the development of scalable data pipelines, manage production datasets, and mentor engineers while collaborating with Data Science and Analytics teams.