Top Data Engineer Jobs in Poland

Lead migration and modernization of legacy analytics to Microsoft Fabric: design lakehouse architectures, build ingestion pipelines, implement governance, CI/CD and data quality, and deliver enterprise-scale data migrations while liaising with stakeholders.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Designs, builds, and maintains scalable ETL/ELT pipelines, data models, storage solutions, and data workflows, primarily using AWS. Ensures data integrity, governance, security, observability, reliability, and performance across structured and unstructured data systems. Collaborates with product, engineering, analytics, and machine learning teams to support data-driven initiatives and optimize analytical and operational data infrastructure.

Build and optimize scalable data pipelines, lakehouse datasets, and data models using decentralized data ownership and Medallion architecture. Partner with product, engineering, analytics, and business teams to deliver data projects, improve data quality, enable product integrations, promote privacy-by-design, and mentor engineers. Help develop agentic systems for data engineering.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

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.

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

Own and maintain the company’s internal data lakehouse, integrating data from BigQuery, ClickHouse, Postgres, and other sources. Build reliable data pipelines and systems that deliver curated data to visualization tools, improve data quality, and support Finance and Business Development decision-making. Partner with Product and Engineering teams on data architecture, modeling, maintenance, and pipeline reliability.

Build and maintain data pipelines and ELT processes, implement data quality metrics and controls using dbt and BigQuery, migrate legacy data quality solutions, integrate with GCP services, optimize execution performance, support testing, and document technical and operational processes. Collaborate with data modelers, business analysts, and test engineers on data transformation and monitoring solutions.

Leads development of complex data pipelines and ETL processes, designs data architectures, optimizes solution performance, and ensures data quality through testing and documentation. Collaborates with product, design, AI/ML, and engineering teams to deliver integrated data solutions. Provides technical guidance, conducts code reviews, stays current with emerging technologies, and supports reliable, scalable data platforms.

Designs and optimizes Google Cloud data platforms, ETL/ELT pipelines, Spark workloads, Airflow DAGs, and event-driven orchestration. Manages Bronze, Silver, and Gold data layers in Google Cloud Storage, implements reprocessing and backfill capabilities, improves metadata-driven tooling, and optimizes performance, cost, observability, and reliability. Collaborates with platform, analytics, and DevOps teams to support production data pipelines.

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, build, and maintain reliable data pipelines supporting accounting and tax systems. Migrate legacy SQL Server solutions to Databricks and Spark, develop Python REST services and web interfaces, integrate data sources, and create CI/CD and Infrastructure as Code solutions. The role also involves data modeling, workflow orchestration, quality monitoring, architectural decisions, code reviews, documentation, and collaboration with engineering and finance stakeholders under monthly accounting deadlines.

Designs and maintains production-grade ETL/ELT pipelines and data models using Snowflake, SQL, Python, dbt, and orchestration tools. Responsibilities include Snowflake performance optimization, access management, cloud integration, data lake and lakehouse development, CI/CD, testing, and documentation. The role collaborates with technical and business stakeholders in German and supports enterprise reporting, automation, and AI-ready data solutions.

Build and support batch and streaming data pipelines, curated datasets, and Lakehouse platform capabilities. Responsibilities include data modeling, transformation, optimization, quality validation, reconciliation, testing, monitoring, and production support. The role partners with engineers and data consumers to deliver reliable data products for analytics, operational decision-making, and AI use cases.

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.

Modernize, migrate, and operate production data-platform infrastructure across cloud and enterprise environments. Responsibilities include moving workloads to AWS, supporting Snowflake and Confluent/Kafka, implementing secure governed data access, modernizing orchestration and storage, troubleshooting incidents, coordinating migrations, and delivering auditable infrastructure changes with Terraform, GitLab, monitoring, and documented processes.

Modernize, migrate, and operate data-platform infrastructure across cloud and enterprise environments. The role involves moving workloads to AWS, supporting Snowflake and Confluent/Kafka, enabling secure data access, and improving architecture, orchestration, storage, and operations. Responsibilities include production troubleshooting, incident response, documentation, runbook creation, migration coordination, and delivering auditable infrastructure changes using Terraform, GitLab, monitoring, and secure access practices.

Modernize, migrate, and operate production data-platform infrastructure across cloud and enterprise environments. Responsibilities include moving workloads to AWS, supporting Snowflake and Confluent/Kafka, implementing secure and governed data access, improving orchestration and operational practices, and using Terraform, GitLab, monitoring, and documented processes. The role also involves troubleshooting incidents, creating runbooks, coordinating migrations, and supporting development, staging, and production environments.

Build and operate an AWS-based data platform that ingests Kafka events into Apache Iceberg tables. Responsibilities include data modeling, schema governance, privacy controls, access management, query services, infrastructure as code, testing, CI, performance and cost optimization, and migrating existing reports. The role also contributes to curated datasets, BI capabilities, and an AI analytics assistant while collaborating with data-producing teams.

Designs, builds, and operates reusable SAP data products using SAP Datasphere and SAP Business Data Cloud. Evaluates extraction and integration approaches, modernizes SAP BW and BW/4HANA data models, and develops future-state data architectures. Collaborates with architects, consultants, business experts, and IT teams while applying expertise in SAP source systems, ABAP, SQL, semantic modeling, data quality, and data harmonization.

Lead the technical direction of a Databricks-based data platform by building reusable frameworks, automation, testing standards, and reliable data pipelines. Improve data quality, lineage, observability, schema management, developer experience, and customer onboarding. Apply AI responsibly to engineering workflows, guide architectural decisions, mentor engineers, and establish scalable platform practices across teams.

Lead the architecture and hands-on development of cloud-native data platforms, real-time and batch pipelines, data products, and scalable storage systems. Work with Databricks, Snowflake, AWS, GCP, Spark, Flink, and related technologies. Provide technical direction, establish engineering standards, mentor engineers, ensure data quality and security, and advise clients on pragmatic platform and architecture decisions.

Lead the design and development of scalable Java backend services, microservices, data integrations, event-driven applications, and distributed systems. Remain hands-on with coding, data processing, APIs, and integration services while guiding engineers and contractors through technical reviews and problem-solving. Collaborate on full-stack and AI-enabled solutions, establish engineering standards, and promote testing, CI/CD, observability, security, resilience, mentoring, and sustainable internal ownership.

Maintain and improve large-scale data pipelines, transformations, models, and workflows. Optimize SQL performance, enhance reliability and scalability, modernize legacy systems, and contribute to architectural decisions. Collaborate with engineering teams on cybersecurity data infrastructure supporting analytics, monitoring, and threat detection.