Top Data Engineer Jobs in Poland

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.

Designs, builds, and supports GCP-based data ecosystems, pipelines, storage architectures, and data models. The role gathers requirements, works directly with clients, optimizes queries and infrastructure, troubleshoots high-impact data issues, monitors system performance, and implements data integration and transformation solutions. It also involves mentoring junior data engineers and collaborating with cross-functional teams and distributed stakeholders.

Own the financial audit data workstream for Printful and Printify. Deliver accounting reconciliations, investigate transaction discrepancies, build Snowflake transformations, extend Looker models, collaborate with Accounting and auditors, and document pipelines, corrections, and edge cases against fixed deadlines.

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, own, scale, and iterate data processing pipelines for Mapbox Roads. Implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate in a cross-functional team to build navigation and high-definition mapping features.

Build and operate production data pipelines, dbt models, Airflow DAGs, and AWS infrastructure as code. Own ingestion, transformation, orchestration, monitoring, data quality, observability, and CI/CD. Support AI-ready data initiatives and Redshift access, collaborate with stakeholders, and provide end-to-end ownership from requirements through troubleshooting. The role requires strong Python, SQL, AWS, analytical data modeling, infrastructure-as-code, and autonomous problem-solving skills.

Build and operate production data pipelines covering ingestion, transformation, orchestration, monitoring, and data quality. Develop dbt models, author and debug Airflow DAGs, provision AWS infrastructure with infrastructure-as-code, and maintain CI/CD using GitHub Actions. Support AI-ready data initiatives and Redshift access, while owning requirements through deployment and incident resolution. The role requires strong Python, SQL, AWS, analytical data modeling, autonomy, and stakeholder collaboration.

Design, implement, and optimize ETL/data pipelines, data quality metrics, APIs, and enrichment for AI/ML platforms; research big-data technologies; run trials, write technical documentation, and collaborate in project teams.

Looking for a Data Engineer to implement cloud-native data platforms, engineer scalable pipelines, and manage cloud resources. Ideal candidates have strong Python and SQL skills, and experience with tools like Spark and Terraform. Familiarity with MLOps is a plus.

The Data Engineer will optimize backend services for a SaaS platform, develop data pipelines using SQL and Python, and collaborate in an Agile environment to implement cloud solutions on Microsoft Azure.

Design and implement scalable batch and streaming data pipelines on Databricks and cloud platforms; build data transformations, CI/CD and MLOps practices; collaborate with data scientists; create data governance, monitoring, and Power BI visualizations; translate business requirements into performant technical solutions.

Design, build, and maintain scalable data pipelines and platforms for batch and streaming data using Spark, Airflow, Iceberg, and cloud services. Optimize ingestion, processing, and storage, collaborate with stakeholders, monitor platform performance, and write high-quality Python/Java/Scala code to support enterprise reporting and analytics.

Design, build, and maintain scalable Azure-based data platforms using Microsoft Fabric, Databricks, Data Lake and ETL/ELT pipelines. Create data models, improve performance and cost efficiency, troubleshoot issues, collaborate with business stakeholders, mentor junior engineers, and stay current with Azure best practices.

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

Build and operate production-grade batch and real-time data pipelines on GCP (BigQuery, Airflow, Kafka), ensure data quality via contracts and observability, develop connectors and integrations, support ML feature pipelines, and mentor junior engineers while contributing to engineering standards and CI/CD practices.

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.

Build and maintain Databricks data pipelines, curated datasets, metric views, and governed analytics systems. Develop Genie Agents and multi-agent orchestration, implement MLflow tracing and evaluation, integrate AI tools through MCP, and collaborate with data and product teams to create reusable business definitions and reliable production-grade analytical solutions.

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.