Top Data Engineer Jobs in Poland

Own and continuously improve Creditstar’s AWS and Snowflake data platform, ensuring scalability, reliability, security, governance, observability, and cost efficiency. Establish engineering standards, manage integrations, support GDPR and PII controls, lead incident resolution, and collaborate with DevOps, BI, analytics, data science, risk, and product teams. The role also supports self-service data capabilities and FinOps practices.

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.

Develop and support software for data logging, visualization, and processing. Responsibilities include gathering requirements, preparing designs, implementing modules, testing work products, analyzing logs and defects, supporting customers, solving engineering problems, and coordinating software project activities. The role requires C++, Python, Bash, Linux, ASAM MDF knowledge, and familiarity with automotive communication protocols such as CAN, Ethernet, and LIN.

Lead the architecture and hands-on delivery of an Everything-as-Code data mesh, including Kafka and NiFi event streaming, Kubernetes infrastructure, Terraform and GitOps deployment, schema governance, hybrid lakehouse integration, FinOps optimization, resiliency, and distributed observability across multi-cloud and on-premises environments.

Designs, develops, deploys, and maintains big data engineering solutions processing billions of healthcare records. Builds and optimizes ETL pipelines in Databricks using Spark, Azure, Delta, and Parquet; performs data profiling, integration, quality, and warehousing activities; and contributes to system design, testing, code reviews, and technical standards. Provides technical guidance and mentoring to junior and mid-level engineers while supporting Agile delivery and production operations.

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.

Design and develop scalable full-stack applications, MVPs, prototypes, backend services, and modern frontend solutions. Integrate APIs, databases, and cloud services while collaborating with product owners, architects, AI engineers, and business stakeholders. Translate innovation concepts into software, support emerging technology experimentation, and follow engineering best practices including testing, code reviews, documentation, and version control. The role supports multiple client-facing projects and requires adaptability and travel as needed.

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.

Owns cloud infrastructure, real-time event-driven data pipelines, distributed storage, networking, and high-availability systems for a high-concurrency gaming platform. Designs Terraform-managed AWS environments, Kafka and CDC pipelines, data lakes, API gateways, automated failover, zero-downtime routing, observability, disaster recovery, cost optimization, and scaling strategies for live-betting transactional workloads.

Designs, develops, and maintains scalable Azure and on-premises data pipelines, enterprise data products, ETL/ELT processes, and data warehouses. Uses Azure Data Factory, Synapse, ADLS, Databricks, SQL Server, SSIS, SQL, and Python. Builds CI/CD pipelines with Azure DevOps and GitHub, optimizes performance and data quality, troubleshoots production issues, documents solutions, and collaborates with stakeholders in Agile teams.

Design and build production-ready AI solutions, including RAG pipelines, autonomous agents, and MCP integrations. Shape AI architecture, infrastructure, scalability, and reliability while integrating enterprise data. Participate in Scrum planning, conduct code reviews, and maintain documentation and testability. The role requires deep Python expertise, distributed systems knowledge, container orchestration, databases, caching, observability, and LLM orchestration frameworks.

Lead the technical strategy, architecture, and delivery of Cisco’s Partner Hub Analytics ecosystem. Design scalable data pipelines using Pinot, Iceberg, Flink, and Spark; establish standards for data quality, observability, governance, performance, and reliability; guide engineering teams; mentor engineers; and collaborate with product and business leaders on high-scale analytical solutions.

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

Build and maintain large-scale data infrastructure for a real-time AdTech platform. Responsibilities include developing streaming and batch ingestion pipelines, feature tables, labeling systems, experimentation infrastructure, data-serving flows, quality frameworks, historical backfills, and privacy controls. The role requires production-grade SQL and Python engineering, Spark-based processing, cloud data warehouse expertise, point-in-time correctness, and collaboration on platform architecture and operational reliability.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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.

Lead architecture and technical standards for a greenfield, enterprise-scale data platform supporting analytics, operational products, and AI workloads. Design Iceberg-based event flows, canonical data models, batch and near-real-time pipelines, governance, data quality, observability, tenant isolation, and regional data strategies. Evaluate platform technologies, optimize performance and costs, mentor engineers, and collaborate with leadership and platform teams to deliver a production-ready foundation.

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.

Develops and maintains Microsoft Lists, Power Automate flows, Excel-based analysis tools, and Power BI semantic models, reports, dashboards, and paginated reports. Responsibilities include automating data processing, designing linked lists, improving data-entry workflows, manipulating multi-source data with Power Query, creating DAX measures, managing relationships, and supporting end-user customization of Power BI visuals. Requires a relevant bachelor’s degree and 3–4 years of experience developing data tools for electronics components.

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.