Top Data Engineer Jobs in Poland

Develop backend and data solutions while supporting daily operations, collaborating with cross-functional stakeholders, improving workflows, monitoring performance metrics, tracking milestones, solving complex challenges, maintaining documentation, and ensuring compliance. The role requires strong communication, prioritization, independent troubleshooting, and the ability to deliver high-quality outcomes in a fast-paced environment.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Design, build, and maintain Azure Data Factory pipelines and ETL/ELT processes; monitor and optimize ADF; develop Python components (Azure Functions, API integrations); support Azure SQL/Synapse warehousing and occasional Power BI reporting; contribute to Azure DevOps CI/CD and collaborate with Product Owner and Architect to deliver scalable data platform improvements.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Own the end-to-end architecture and implementation of a scalable data platform processing real-time IoT telemetry and predictive analytics. Build Kafka-based streaming and batch pipelines, Kubernetes workloads, AWS infrastructure, relational data models, integrations, and CI/CD systems. Lead performance, reliability, observability, privacy, and compliance efforts in regulated healthcare environments, while applying AI-assisted engineering practices and participating in 24/7 on-call support.

Design, build, and maintain scalable data pipelines for BigQuery migrations. Develop ingestion and transformation frameworks, orchestrate and optimize workloads, ensure data quality, and deliver production-grade data products for reporting and analytics. Collaborate with analytics engineers, architects, and stakeholders on platform migration and sustainability. Required capabilities include Python, SQL, BigQuery, ETL/ELT, data modeling, orchestration, Git, CI/CD, cloud-native architectures, testing, monitoring, and data validation.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

Lead design, build, and maintenance of an enterprise semantic hub and knowledge graph. Integrate diverse data/metadata, define ontologies, build APIs and MCP endpoints for LLMs, optimize ingestion/query performance, and embed FAIR principles. Collaborate with data owners and AI teams to translate business needs into schemas and scalable, secure deployments in Kubernetes/GitOps environments.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Build and operate large-scale web data extraction and scraping systems, design ingestion and processing pipelines, ensure data quality and governance, optimize performance and reliability, implement observability, and collaborate with product and engineering teams to deliver production-grade APIs, backend services, and cloud-deployed workflows.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Build and maintain scalable HR data solutions using Databricks, Spark, SQL and Python. Improve data quality, support migrations/integrations, implement validation and governance, and translate business requirements for HR, Finance, and country stakeholders.

The Data Engineer will design and maintain data pipelines, optimize performance, enforce governance, and collaborate with AI teams for innovative solutions.

Own the finance data domain by developing financial data products and models, integrating and improving data quality, monitoring freshness and completeness, and resolving reconciliation issues. Build reports, alerts, automations, and LLM agents using Python, SQL, Snowflake, Airflow, and dbt. Communicate data issues clearly to Finance leadership and ensure financial reporting and planning data remains accurate and reliable.

The Senior Data Engineer will develop databases, data lakes, and data ingestion pipelines, ensuring scalable solutions and engaging with stakeholders across various departments.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Analyze user behavior on websites and apps, focusing on KPIs and data visualization. Build dashboards, run A/B tests, and communicate findings to stakeholders.

Owner of all PostgreSQL databases underpinning a multi-site telecom platform. Responsibilities include DB installation, HA, backup/restore, performance tuning, large-scale bulk loads/migrations, multi-datacenter replication, data governance (retention, masking, archival), monitoring (Prometheus/Grafana), automation scripting, and cross-platform integration with network-element databases.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.