Top Data Engineer Jobs in Krakow

Build and maintain large-scale distributed data services for log delivery, analytics, reporting, and alerting. Design, develop, test, deploy, and operate scalable services using DevOps practices, observability tooling, Kubernetes, and Kafka. Collaborate with product owners and SREs to diagnose performance, automate processes, and improve reliability for internal and external users.

As a Senior Data Engineer, you'll design and operate data pipelines on GCP, ensuring data quality, mentoring junior engineers, and supporting AI initiatives.

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.

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.

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.

The Senior Data Engineer will design and evolve the internal Data Platform, focusing on architecture, data engineering, and mentoring while ensuring standards for data quality and governance.

Build and maintain high-throughput data pipelines, design schemas and data models, implement IaC and CI/CD, collaborate with Data Science/AI on feature stores and MLOps, monitor containerized systems, and advise stakeholders on data architecture.

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.

Execute and document tests across planned levels for data and ETL pipelines, prepare test data and environments, use test management tools, review requirements and test artefacts, report progress, and collaborate with team members and stakeholders.

Design and build reusable Databricks Lakehouse PySpark pipelines, implement data models (fact/dimension, SCD Type 2), enable incremental/idempotent loads, optimize performance, implement event-driven integrations, collaborate on Azure cloud resources, and improve data platform architecture.

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.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design and implement scalable cloud data infrastructure, ETL pipelines, data models, and warehouses. Develop data processing solutions in Python and SQL, integrate legacy and modern systems, prepare data for AI use cases, gather requirements, produce technical specifications, scope releases, manage code reviews, and liaise with customers.

Design, build, and maintain scalable Databricks ETL/ELT pipelines using Python and SQL. Optimize cloud data workflows, support data models and integrations across Azure/AWS/GCP, monitor and troubleshoot pipelines, and collaborate with analysts and data scientists to deliver clean data for AI and analytics.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Lead design and build of cloud-native, production-grade data platforms and real-time pipelines. Own architecture, implement scalable ingestion, storage, processing, and access layers, ensure security and governance, optimize performance, mentor engineers, and consult directly with clients to deliver data products and solutions.