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.

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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Design and maintain scalable data architectures and pipelines. Collaborate with vendors on data migration, integration, transformation, and governance. Monitor, troubleshoot, document, and enhance data platform operations to support analytics and reporting needs.

Develop and maintain scalable AWS data pipelines, modify Lambda functions and SQL processing jobs, design Iceberg-based data models, optimize Athena and Spark workloads, and implement data quality, observability, testing, governance, and documentation practices. The role also requires collaboration with engineering, product, and data stakeholders, along with GitLab-based version control and CI/CD or Infrastructure as Code familiarity.

Design, build, and maintain scalable data pipelines in cloud environments, ensuring data quality and availability while collaborating with business teams.

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.

Develop and maintain the company data warehouse using cloud technologies. Responsibilities include building EL/ELT/ETL pipelines, synchronizing internal and external systems, creating data collection tools, supporting business users, analysts, and ML engineers, implementing data governance and quality practices, and optimizing infrastructure costs.

Develop and maintain data-driven business solutions using Big Data, analytics, predictive modeling, and machine learning. Integrate structured and unstructured data, create reports and dashboards, administer business applications and databases, and improve system reliability, performance, security, and standardization. Collaborate with vendors, consultants, key users, and support teams while documenting technical and analytical solutions.

Build and maintain Python ETL pipelines, data warehouses, and data lakes supporting marketing analytics and personalized marketing programs. Integrate mobile marketing data, automate scalable and cost-efficient workflows, and ensure data quality, validation, governance, and performance. Collaborate with marketing and studio teams to deliver data-driven solutions. The role uses Snowflake or Firebolt, Airflow, SQL, distributed computing, and modern AI development tools.

Design, build, and maintain large-scale data processing systems and pipelines (Spark, BigQuery, dbt/Dataform). Improve scalability, performance, and reliability; own projects end-to-end; extend and operate Airflow; work with cloud (GCP/AWS), Kubernetes, and CI/CD practices.

Build and maintain reliable data models and ETL/ELT pipelines across AWS-based data lake layers. Ingest high-volume billing, CRM, network, and customer data; process Kafka events; develop dbt transformations; orchestrate workflows with Airflow; and implement data quality, monitoring, documentation, and reconciliation practices. Collaborate with stakeholders and engineers to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures while integrating multiple data sources. Ensure data quality, scalability, and performance, and collaborate with BI teams supporting Power BI and reporting platforms. Participate in cloud data architecture initiatives under senior guidance.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures; integrate multiple data sources; optimize large-scale processing; ensure data quality and performance; and collaborate with BI teams supporting Power BI and reporting platforms under senior guidance.

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 and optimize databases and data processing systems, collaborate with developers, gather user requirements, and provide user support.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Design, build and support scalable, event-driven data ingestion and transformation pipelines in AWS. Develop Kafka/MSK and Glue-based ETL/ELT flows, ensure data quality, implement metadata-driven solutions, support CI/CD and operational monitoring, and collaborate with architects and domain teams to onboard data into the Operational Data Store.

Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.

As a Data Engineer, you'll design robust data pipelines, optimize data architectures, ensure data quality, and support analytics efforts while collaborating with stakeholders.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

Design, build, and operate GitOps-driven data and AI platforms and pipelines (ETL/ELT, feature stores, batch/real-time inference). Enable GenAI/RAG (embeddings, vector ingestion), implement IaC and CI/CD, enforce data quality, governance, observability and MLOps, and support Azure/AWS-based production deployments while collaborating with security, product, and operations teams.

Build and maintain scalable data pipelines and integrations for a centralized data platform. Develop shared components, CI/CD pipelines, and Infrastructure as Code solutions while ensuring data quality, reliability, and consistency. Contribute to architecture decisions, engineering standards, code reviews, documentation, and system integrations. The role requires strong Python, SQL, Spark, ETL/ELT, data modeling, orchestration, monitoring, and relational database skills, with opportunities to work with streaming and lakehouse technologies.

Design, build, and optimize scalable data pipelines, architectures, and reusable data assets for sourcing BI, analytics, and AI. Translate business requirements into data models and technical specifications, integrate and transform raw data, optimize cloud solutions, implement data quality monitoring and access controls, and collaborate with analysts, data scientists, architects, and business stakeholders.