Top Data Engineer Jobs in Krakow

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 migration of a large-scale analytics platform from BigQuery to Databricks. Design and implement Databricks Lakehouse architectures, build and optimize PySpark/Delta Lake pipelines, enforce governance with Unity Catalog, support production deployments, and collaborate with cross-functional teams to ensure scalable, reliable data processing and platform 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.

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

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

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.

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 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.

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.

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, 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.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Maintain and scale production data ingestion pipelines processing billions of events, support and optimize complex ETL and streaming workflows, develop platform tools and ML/streaming initiatives, ensure reliability/observability, and assist data scientists and analysts with production workloads.

Design and build scalable real-time and batch data pipelines for programmatic advertising, ingesting and normalizing SSP/DSP/GAM data, maintaining warehouses, implementing ETL/ELT, ensuring data quality and privacy compliance, and supporting analytics and BI teams.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Develop and enhance BI solutions using SAP technologies, ensuring best practices in performance, data integration, and training users on SAP Data & Analytics.

Design, build, and operate Azure-based data platform environments and lead migrations to Azure/Databricks/Microsoft Fabric. Implement IaC (Terraform), Kubernetes platform operations, CI/CD automation, security and governance controls, monitoring/observability, and cost/performance optimizations. Collaborate with data engineers, architects, and stakeholders and produce technical documentation while providing technical leadership during the migration.

Design, build, and maintain analytics-ready datasets and ETL for cybersecurity reporting; develop scripts and dashboards for tools like CrowdStrike, Prisma/Cortex, and MS InTune; create Power BI on-demand and static reports; translate security objectives into measurable metrics and present to technology leaders.

Lead design and implement CDC-based data architecture (Postgres -> Kafka -> Snowflake). Build streaming/near-real-time pipelines, data quality controls, data contracts, tenant separation, replay/reload mechanisms, and external delivery. Start hands-on and grow a small data engineering team; deliver a pragmatic 3/6/12-month roadmap.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.