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.

Designs, builds, and operates ING’s resilient data analytics fallback platform. Responsibilities include developing Spark, Airflow, Iceberg, Trino, and object-storage solutions; implementing data replication, synchronization, backup, recovery, and disaster-recovery capabilities; improving observability and operational resilience; and supporting critical analytical workloads. The role also contributes to architecture, automation, technical standards, cross-team collaboration, and on-call operations.

Designs and maintains scalable data ingestion, transformation, ETL/ELT, and data quality pipelines using Databricks and cloud platforms. Builds metadata-driven frameworks, supports Bronze-to-Gold data flows, implements testing and reconciliation, and contributes to monitoring, CI/CD, migrations, performance optimization, and operational support. Collaborates with data, BI, platform, architecture, and client teams while supporting enterprise data products and knowledge transfer.

Designs, develops, tests, and maintains data lakehouse workspaces, pipelines, ingestion solutions, data models, dashboards, and reports. Ensures data quality, consistency, governance, and compliance across analytics and AI/ML use cases. Builds scalable AI-driven infrastructure, orchestration layers, and evaluation frameworks for regulated financial environments. Collaborates with cross-functional teams, troubleshoots issues, optimizes solutions, and documents data products and processes.

Designs and operates Planet’s modern data platform using Snowflake, Fivetran, and dbt. Responsibilities include building scalable ETL/ELT and reverse ETL pipelines, dimensional models, data products, quality and observability frameworks, governance practices, and near-real-time architectures. The role leads architectural decisions, troubleshooting, documentation, compliance, and platform optimization while mentoring data engineers and partnering with analysts, AI engineers, and business stakeholders.

Build, operate, and improve data pipelines, backend services, APIs, infrastructure, and cloud platforms supporting analytics and reporting. Modernize Airflow and cloud environments, optimize BigQuery usage and infrastructure costs, maintain Infrastructure as Code, and participate in monitoring, alerting, and incident response. The role also supports a live customer-facing slot-availability application and collaborates with distributed engineering teams.

Build, operate, and improve data pipelines, backend services, APIs, infrastructure, and analytics platforms supporting warehouse automation systems. Responsibilities include Terraform-based infrastructure, Airflow optimization, BigQuery cost efficiency, monitoring, incident response, CI/CD, and maintaining a customer-facing slot-availability application. The role requires collaboration across distributed engineering teams and work across application code, data pipelines, and cloud infrastructure.

Design, develop, and maintain scalable Databricks data pipelines and ETL/ELT workflows using Python and SQL. Optimize cloud data solutions for performance, reliability, and cost efficiency across Azure, AWS, or GCP. Build data models, tables, and integrations; monitor and troubleshoot pipelines; collaborate with analysts, scientists, and cross-functional teams; and support AI, analytics, and MLOps initiatives.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs and maintains CI/CD pipelines, reusable delivery templates, deployment automation, artifact distribution, Docker workflows, and Kubernetes environments. Automates platform engineering activities, supports Python packaging migrations from Poetry or setuptools to UV, and improves software delivery governance, traceability, and repeatability. The role also collaborates with engineering teams on DevOps practices and maintains technical documentation, with opportunities to support GCP-based enterprise data platforms.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Develop Rust-based SQL routing systems, maintain connectors, review open-source contributions, and operate production services. Ensure data integrity under concurrency, retries, and failures while contributing to product direction and on-call support.

Build and maintain high-performance data lakehouse infrastructure using Java and Rust. Develop reliable Apache Iceberg maintenance workflows, including compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Ensure data integrity under concurrency, retries, and failures. Maintain a Rust-based SQL query router, develop connectors, review open-source contributions, support production deployments, and participate in on-call debugging.

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.

Develop and maintain unified observability across Kubernetes, databases, Trino, Hadoop, and Apache Ozone. Build DORA metrics collection and service health dashboards, identify monitoring gaps, optimize Alertmanager rules, reduce alert fatigue, and provide centralized SRE expertise. The role requires strong Prometheus, Grafana, Alertmanager, PromQL, reliability metrics, incident analysis, and distributed-systems knowledge.

Owns end-to-end data platform architecture on Microsoft Fabric, designing Bronze, Silver, and Gold Delta tables, Spark feature pipelines, canonical MERGE workflows, Direct Lake semantic models, and OneLake layouts. The role also covers performance optimization, Fabric capacity sizing, idempotent Python-based writes, IaC collaboration, and risk data product delivery. Responsibilities include handling tokenized payment data, rolling risk features, semantic model performance, and integrations across Fabric workspaces.

Develop and maintain C++ software for data logging, visualization, and processing on Linux platforms. Design and execute software tests, troubleshoot defects, prepare technical documentation, support deployed tools, and collaborate with software, hardware, and systems integration teams. The role also involves implementing standards-compliant solutions, resolving project technical issues, and continuously improving technical expertise.

Design and implement scalable Databricks data pipelines, standardize and migrate legacy pipelines, develop dbt and Python transformations, onboard regional datasets, optimize performance and resource usage, and collaborate with architects and analytics teams. The role also contributes to future Data Science and MLOps foundations while supporting cloud-based data platforms and data modeling practices.

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.

Designs and develops Databricks data pipelines and reusable governed data products. Implements Unity Catalog governance, integrates SAP data, and develops PySpark and SQL solutions. Supports data modeling, source-to-target mapping, quality controls, Git-based development, testing, deployment, and CI/CD. Collaborates directly with business, data, platform, and client stakeholders in German, English, and Polish while documenting solutions and transferring knowledge.

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.