Top Data Engineer Jobs in Poland

Designs and builds production-grade AI applications natively on Snowflake using Cortex Analyst, Cortex Search, Snowflake Agents, semantic models, Python, and SQL. Develops secure, performant natural-language querying, retrieval, text-to-SQL, and agentic workflows over governed data. Owns delivery of a technical workstream, collaborates with data scientists, engineers, clients, and stakeholders, and supports enterprise GenAI and analytics solutions. Snowflake implementation experience is mandatory; regulated-industry experience is advantageous.

Maintain and support a production Databricks Data Lake, including transformations, reports, pipelines, and data quality. Monitor platform health, investigate and resolve data issues, maintain Python transformation logic, support end users and stakeholders, and keep technical documentation current. The role focuses on independently operating and improving an established data platform rather than building one from scratch.

Migrate legacy Oracle SQL and PL/SQL business logic to BigQuery and dbt on GCP. Develop, test, optimize, and maintain cloud data processing solutions; validate consistency between legacy and new implementations; implement customer record grouping and identification; and prepare for Golden Record capabilities. Collaborate with architects, analysts, data owners, and platform teams while maintaining technical documentation and using AI tools to accelerate modernization.

Develop and maintain GCP lakehouse Gold and Platinum layers, build dbt and PySpark transformations, and design Airflow pipelines. Optimize BigQuery performance and costs, manage Terraform infrastructure, implement GitHub Actions CI/CD, and support IAM, DLP, Dataplex, data governance, metadata, and lineage practices. Collaborate with stakeholders and engineering teams to deliver reliable, secure data platforms while contributing to code reviews and technical consulting.

Designs, builds, and maintains scalable data pipelines and processing frameworks for analytics and AI. The role uses Snowflake and cloud platforms to ingest, transform, orchestrate, and optimize structured and unstructured data. Responsibilities include supporting data warehouse, lake, lakehouse, and mesh architectures; contributing to CI/CD, infrastructure automation, data quality, troubleshooting, POCs, and Snowflake practice development. Collaboration with stakeholders and technical teams is required.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Own and evolve a production lakehouse supporting millions of daily financial events. Design bronze, silver, and gold layers; build CDC streaming pipelines; optimize storage and queries; manage retention, archival, reconciliation, and data correctness. Establish governance through cataloging, lineage, access controls, masking, encryption, and audit trails. Monitor ingestion health, anomalies, and cloud costs while supporting reliable, traceable data for Analytics, Finance, and Regulatory teams.

Design and deliver enterprise-scale Snowflake data pipelines, ingestion processes, transformations, curated datasets, data quality checks, monitoring, documentation, and operational handover for a regulated financial institution. Collaborate with governance, architecture, client, and engineering teams on metadata, lineage, access controls, deployment, stabilization, tuning, and knowledge transfer. The one-year engagement requires relocation from Poland to the Middle East.

Build and maintain scalable data pipelines for aviation predictive-maintenance solutions. Develop ETL/ELT processes using Azure Databricks, Apache Spark, Python, and SQL; implement CI/CD with Azure tooling, Terraform, and Databricks Asset Bundles; optimize Spark workloads; validate data quality; troubleshoot pipelines; document solutions; and collaborate with engineers and stakeholders to enhance the data platform.

Manage product and costing data in SAP, create product configurators and material codes, maintain data quality, upload data to cloud systems, and phase out obsolete records. Calculate standard prices, support pricing and deal analysis, and collaborate with product management and engineering on new product introductions. Lead data-management improvements and contribute to cross-factory data engineering initiatives.

Design and develop scalable data ingestion pipelines, backend services, REST APIs, and data-processing systems using Python and Django. Build reliable integrations from file and API sources into cloud data platforms, with strong emphasis on validation, data quality, error handling, testing, scalability, and production readiness. Collaborate with stakeholders, maintain CI/CD environments and documentation, use workflow orchestration tools such as Airflow, and contribute to cloud-based data engineering projects.

Build and maintain scalable ETL/ELT pipelines and data processing on Azure Databricks. Develop data infrastructure supporting customer features, AI/ML, analytics, and reporting. Contribute to Terraform, infrastructure as code, DevOps, reliability, security, performance, data modeling, governance, and quality standards. Help shape the platform backlog, identify improvements, and establish scalable data patterns across Medius products.

Design, build, test, deploy, and operate scalable Azure and Databricks data pipelines and analytical data products. Refactor pipelines for reliability, performance, observability, and maintainability; diagnose incidents; and implement SRE, DataOps, CI/CD, testing, monitoring, governance, and security practices. Collaborate with data, architecture, product, and engineering teams to onboard workloads, translate reporting needs into technical solutions, document data flows, and share engineering practices.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Develop and maintain scalable AWS data engineering platforms and reusable Python modules. Build object-oriented data processing solutions, debug pipelines across Step Functions, Glue, Athena, and Lambda, and apply modern engineering practices including testing, CI/CD, and version control. Collaborate with agile teams to deliver reliable solutions across countries and business environments.

Designs, builds, maintains, and optimizes enterprise data platforms, pipelines, warehouses, models, and integration frameworks supporting Power BI, analytics, AI, and operational reporting. The role develops Azure-based ETL/ELT solutions, improves data quality and reliability, establishes governance and standardized metrics, and collaborates with BI, IT, automation, and AI teams. It is a hybrid position in Katowice, Poland.

Design and implement Azure-based data processing systems, scalable data pipelines, data warehouses, data lakes, and ETL workflows. Transform and optimize structured and unstructured data, tune queries, improve storage and retrieval performance, and resolve system bottlenecks. Collaborate with data scientists, analysts, and stakeholders to deliver technical solutions while working within Agile methodologies.

Designs, builds, and supports GCP data ecosystems, including cloud storage, processing, warehouses, data lakes, pipelines, and integrations. Responsibilities include requirements gathering, data modeling, performance optimization, troubleshooting, monitoring, root cause analysis, and client collaboration. The role also mentors junior data engineers and contributes to ETL/ELT, orchestration, transformation, and database management initiatives.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional teams.

Design, implement, and maintain scalable streaming and batch data pipelines using Apache Pinot, Iceberg, Flink, and Spark. Improve pipeline performance, reliability, observability, and data quality while participating in software development, testing, incident resolution, and postmortems. Collaborate with engineering, product, and data teams, use AI coding tools, and continuously develop expertise in production data systems.

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 and develop Azure-based data platforms, ETL/ELT processes, data warehouses, data marts, and data lakes. Write advanced SQL and Python, create Power BI visualizations, model data, and work with Databricks and Spark. Deploy data applications using Docker, Kubernetes, and GitHub while contributing to scalable client projects and maintaining data quality, monitoring, governance, and operational readiness.