Top Data Engineer Jobs in Krakow

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Design, build, and maintain ELT/ETL pipelines into Snowflake, produce governed Power BI analytics, implement data quality/observability, optimize data models for performance and cost, and use AI tools to accelerate development while collaborating across product and finance teams.

Design, build, and maintain ELT/ETL pipelines into Snowflake, model data for analytics, deliver Power BI reports, embed data quality and observability, and use AI tools to accelerate development while collaborating across engineering, product, and finance.

Designs and maintains ETL/ELT pipelines, data models, and data layers using SQL, Python, PySpark, Databricks, Azure, and Delta Lake. Ensures data quality through validation, monitoring, alerting, and incident diagnosis; optimizes processing performance and costs; documents data processes and lineage; and delivers trusted data for analytics, reporting, automation, and product teams. The role also translates business requirements into technical solutions and responsibly verifies AI-assisted work.

Designs and optimizes scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Builds agentic workflows for data validation, troubleshooting, automation, and production operations. Develops reusable frameworks, implements data quality and monitoring controls, optimizes workloads, supports deployment and productionization, and resolves complex data issues. Collaborates with business, data engineering, and platform teams to identify automation opportunities.

Develops in-vehicle data quality checks, validation-drive automation, data insights, ETL solutions, APIs, data pipelines, and processing tools for autonomous driving systems. Collaborates with engineering, product, data science, and domain teams while adapting big-data technologies for cloud platforms. Requires Python, C++ or Rust, SQL, scripting, machine learning, data preparation, statistics, and debugging expertise. Experience with cloud, containers, embedded AI, automotive datasets, Git, CI/CD, and ISO 26262 is beneficial.

Builds data engineering solutions for in-vehicle data quality, validation-drive automation, data triaging, and insights supporting hands-off urban driving. Responsibilities include developing APIs, cleaning and organizing raw and processed data, implementing analytics and machine learning solutions, and collaborating with engineering, product, and domain teams. The role involves cloud-based ETL and big-data systems connected to embedded driver-assistance technologies.

Build and operate production data pipelines covering ingestion, transformation, orchestration, monitoring, and data quality. Develop dbt models, author and debug Airflow DAGs, provision AWS infrastructure with infrastructure-as-code, and maintain CI/CD using GitHub Actions. Support AI-ready data initiatives and Redshift access, while owning requirements through deployment and incident resolution. The role requires strong Python, SQL, AWS, analytical data modeling, autonomy, and stakeholder collaboration.

Own end-to-end data readiness across ingestion, validation, transformation, aggregation, extraction, and delivery workflows. Develop and optimize Python, PySpark, SQL, Databricks, ADF, and Delta Lake solutions; investigate data-quality and reconciliation issues; coordinate validation with stakeholders and providers; resolve complex pipeline and platform incidents; review engineering work; guide junior engineers; and maintain documentation, contracts, runbooks, and deployment procedures.

Build and maintain scalable ETL/ELT pipelines and data processing on Azure Databricks. Develop data infrastructure supporting customer features, AI/ML, analytics, and reporting. Contribute to Terraform, infrastructure as code, DevOps, reliability, security, performance, data modeling, governance, and quality standards. Help shape the platform backlog, identify improvements, and establish scalable data patterns across Medius products.

Supports enterprise data pipelines through monitoring, ingestion, validation, troubleshooting, testing, documentation, and minor enhancements. Works with Python, PySpark, SQL, APIs, files, cloud data platforms, and data quality processes under senior guidance. Contributes to source onboarding, operational support, Git workflows, code reviews, and deployment practices in a six-month engagement.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Design, implement, and maintain scalable streaming and batch data pipelines using Apache Pinot, Iceberg, Flink, and Spark. Improve pipeline performance, reliability, observability, and data quality while participating in software development, testing, incident resolution, and postmortems. Collaborate with engineering, product, and data teams, use AI coding tools, and continuously develop expertise in production data systems.

Maintain and support a production Databricks Data Lake, including transformations, reports, pipelines, and data quality. Monitor platform health, investigate and resolve data issues, maintain Python transformation logic, support end users and stakeholders, and keep technical documentation current. The role focuses on independently operating and improving an established data platform rather than building one from scratch.

Migrate legacy Oracle SQL and PL/SQL business logic to BigQuery and dbt on GCP. Develop, test, optimize, and maintain cloud data processing solutions; validate consistency between legacy and new implementations; implement customer record grouping and identification; and prepare for Golden Record capabilities. Collaborate with architects, analysts, data owners, and platform teams while maintaining technical documentation and using AI tools to accelerate modernization.

Develop and maintain GCP lakehouse Gold and Platinum layers, build dbt and PySpark transformations, and design Airflow pipelines. Optimize BigQuery performance and costs, manage Terraform infrastructure, implement GitHub Actions CI/CD, and support IAM, DLP, Dataplex, data governance, metadata, and lineage practices. Collaborate with stakeholders and engineering teams to deliver reliable, secure data platforms while contributing to code reviews and technical consulting.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Own and evolve a production lakehouse supporting millions of daily financial events. Design bronze, silver, and gold layers; build CDC streaming pipelines; optimize storage and queries; manage retention, archival, reconciliation, and data correctness. Establish governance through cataloging, lineage, access controls, masking, encryption, and audit trails. Monitor ingestion health, anomalies, and cloud costs while supporting reliable, traceable data for Analytics, Finance, and Regulatory teams.

Design and deliver enterprise-scale Snowflake data pipelines, ingestion processes, transformations, curated datasets, data quality checks, monitoring, documentation, and operational handover for a regulated financial institution. Collaborate with governance, architecture, client, and engineering teams on metadata, lineage, access controls, deployment, stabilization, tuning, and knowledge transfer. The one-year engagement requires relocation from Poland to the Middle East.

Design, build, test, deploy, and operate scalable Azure and Databricks data pipelines and analytical data products. Refactor pipelines for reliability, performance, observability, and maintainability; diagnose incidents; and implement SRE, DataOps, CI/CD, testing, monitoring, governance, and security practices. Collaborate with data, architecture, product, and engineering teams to onboard workloads, translate reporting needs into technical solutions, document data flows, and share engineering practices.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.