Top Data Engineer Jobs in Warsaw

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.

Lead a data engineering team, develop AI self-service portal and data pipelines, interface with internal teams, and ensure software engineering best practices.

Design, build, and operate reliable, scalable distributed data infrastructure and connectors. Develop RESTful services, address multi-threading and fault-tolerance, collaborate across teams, and contribute to Netflix OSS and platform tooling.

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.

Design, build, and operate scalable, fault-tolerant distributed data infrastructure and internal data platform products (e.g., ZooKeeper, connectors, Experimentation platform). Own end-to-end systems, improve reliability and scalability, collaborate cross-functionally, contribute to Netflix OSS, and support automated integration testing for batch and streaming connectors.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Build and optimize scalable consumer data pipelines, models, schemas, and cloud data lake solutions using Snowflake, dbt, and Python. Ensure data quality, security, privacy compliance, and performance while enabling analytics, reporting, segmentation, and personalization. Collaborate with architects, product owners, and global teams to integrate diverse data sources and implement batch and streaming solutions.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Design and maintain data pipelines, ensure data reliability, structure data systems, and manage software development processes while collaborating in a team.

Build and define company-wide Snowflake data assets and metrics layers. Architect, model, and deploy reliable DBT/Snowflake data pipelines and semantic metrics for reporting, analysis, and self-service. Collaborate with data engineering and stakeholders to implement scalable metrics, dimensions, and best practices.

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.

Develop software for risk data pipelines, collaborating with trading teams to manage trade data, improve capital efficiency, and automate operations.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, develop, and deploy data solutions and analytics pipelines using Spark/Databricks and Python. Build data models, ETL/ingestion (batch and near real-time), optimize dashboards and queries (SQL, DAX), tune performance for large volumes, and improve platform robustness while mentoring teammates and contributing to unit community.

Design, build, and deploy Power BI solutions and data models; optimize SQL and DAX performance; manage ETL and real-time ingestion; ensure data quality and warehousing best practices; mentor teammates and contribute to large distributed system design and delivery.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and maintain backend services, REST APIs, databases, and near-real-time and batch data pipelines. Develop a query engine and scalable insights pipelines, collaborate with product and engineering, improve legacy code, and ensure production monitoring, alerting, and tooling for support.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

Design and build a scalable cloud data platform on Google Cloud, create reliable batch and streaming ETL/ELT pipelines, enforce data quality and observability, automate IaC/CI-CD, and support analytics, ML, and product teams with high-quality data.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.