Top Data Engineer Jobs in Warsaw

Build, maintain, troubleshoot, and improve production data pipelines and analytical data models. Ingest data into AWS Data Lake, create fact and dimension models, validate and monitor pipelines, and backfill data when needed. Partner with analysts and business stakeholders to translate requirements into reliable datasets, clarify ambiguity, investigate issues, and deliver pragmatic solutions. Use AI-assisted engineering tools while critically reviewing generated code.

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.

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, develop, and optimize GPU- and CPU-accelerated image and data processing libraries. Implement and performance-tune parallel numerical algorithms, benchmark across architectures, contribute to technical roadmaps, provide technical leadership, and collaborate with product and engineering teams to meet feature and performance requirements.

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.

Build and manage data pipelines, storage, refresh processes, dbt models, curated datasets, and semantic models across Microsoft Fabric and AWS. Establish Kimball dimensional modeling standards, support Power BI analytics, and maintain data quality through automated validation, monitoring, alerting, and troubleshooting. Document pipelines, datasets, lineage, and runbooks while enforcing secure access, version control, code reviews, environment separation, and controlled releases. Collaborate effectively with technical and non-technical stakeholders.

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.

Develop, operate, and optimize a multi-tenant Azure data platform using Data Factory, ADLS Gen2, Databricks, and Synapse Analytics. Build scalable PySpark and Spark ETL/ELT pipelines, maintain data infrastructure, implement CI/CD automation, optimize cost and performance, and support data governance, security, modeling, and reporting. Collaborate with BI and data teams across development, test, and production environments.

Build and maintain a cloud-based data platform, incremental ELT pipelines, and Delta Lakehouse solutions using Python, PySpark, and Databricks. Manage data lineage, cataloging, governance, testing, CI/CD, monitoring, documentation, and production deployments. Collaborate with data and engineering teams, mentor colleagues, establish data project methodologies, and support data-driven product and business decisions.

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.

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.

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.

Own and operate the ClickHouse data warehouse and ETL pipelines; build and maintain dashboards and BI layer; provide proactive analyses (cohorts, unit economics, attribution); implement monitoring/alerting; work closely with co-founders and cross-functional leaders to make data the single source of truth.

Lead a data engineering team while designing and implementing scalable data solutions, pipelines, models, and quality processes. Support forecasting, machine learning, web application, optimization, and analytics platforms. The role includes technical leadership, people management, troubleshooting, documentation, coding standards, and collaboration with data scientists, AI engineers, DevOps engineers, architects, and platform teams.

As a Data Engineer, you will develop and support big data processing pipelines, build cloud-based infrastructure, and provide visualization solutions for data assets.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

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.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.

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, develop, and optimize scalable data pipelines on Snowflake. Collaborate with cross-functional teams, implement data modelling and performance best practices, ensure data quality/security, automate workflows, integrate diverse data sources, and monitor/troubleshoot pipelines.

Build ShipMonk's AI domain: consult with stakeholders to identify real problems, design and implement AI/ML projects from prototype to production, make AI tools accessible to non-technical teams, and balance technical complexity with business impact.

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.