Top Data Engineer Jobs in Krakow

Design, build, and optimize Snowflake-based cloud data platforms and scalable ELT/ETL pipelines for financial-services clients. The role involves data integration, modeling, governance, performance and cost optimization, advanced Snowflake capabilities, stakeholder collaboration, technical leadership, mentoring, and delivery of client projects in an Agile environment. The one-year engagement requires relocation to Qatar.

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.

Data Engineer responsible for migrating approximately 1,000 datasets to Snowflake, adapting data pipelines, and maintaining data quality and reliability. The role supports Airflow workflow scheduling and monitoring, Airflow 2-to-3 upgrades, Confluent region migration, and migration to Iceberg. Responsibilities require hands-on Python, advanced SQL, Snowflake modeling, Airflow, Kafka, Spark, AWS S3, testing, debugging, and large-scale data experience.

Own and maintain the company’s internal data lakehouse, integrating data from BigQuery, ClickHouse, Postgres, and other sources. Build reliable data pipelines and systems that deliver curated data to visualization tools, improve data quality, and support Finance and Business Development decision-making. Partner with Product and Engineering teams on data architecture, modeling, maintenance, and pipeline reliability.

Build and maintain data warehouse solutions using Snowflake, SQL, dbt, and dimensional modeling. Develop, test, and deploy data models; manage code with Git and Bitbucket; and support CI/CD workflows, containerized deployments, and automation using Python. The role also requires German proficiency and may involve Azure services, orchestration tools, finance data products, and requirements engineering.

Build and maintain data pipelines and ELT processes, implement data quality metrics and controls using dbt and BigQuery, migrate legacy data quality solutions, integrate with GCP services, optimize execution performance, support testing, and document technical and operational processes. Collaborate with data modelers, business analysts, and test engineers on data transformation and monitoring solutions.

Designs and optimizes Google Cloud data platforms, ETL/ELT pipelines, Spark workloads, Airflow DAGs, and event-driven orchestration. Manages Bronze, Silver, and Gold data layers in Google Cloud Storage, implements reprocessing and backfill capabilities, improves metadata-driven tooling, and optimizes performance, cost, observability, and reliability. Collaborates with platform, analytics, and DevOps teams to support production data pipelines.

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 operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

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.

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.

Lead the architecture and hands-on development of cloud-native data platforms, real-time and batch pipelines, data products, and scalable storage systems. Work with Databricks, Snowflake, AWS, GCP, Spark, Flink, and related technologies. Provide technical direction, establish engineering standards, mentor engineers, ensure data quality and security, and advise clients on pragmatic platform and architecture decisions.

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.

Designs, develops, tests, and maintains data lakehouse workspaces, pipelines, ingestion solutions, data models, dashboards, and reports. Ensures data quality, consistency, governance, and compliance across analytics and AI/ML use cases. Builds scalable AI-driven infrastructure, orchestration layers, and evaluation frameworks for regulated financial environments. Collaborates with cross-functional teams, troubleshoots issues, optimizes solutions, and documents data products and processes.

Build, operate, and improve data pipelines, backend services, APIs, infrastructure, and cloud platforms supporting analytics and reporting. Modernize Airflow and cloud environments, optimize BigQuery usage and infrastructure costs, maintain Infrastructure as Code, and participate in monitoring, alerting, and incident response. The role also supports a live customer-facing slot-availability application and collaborates with distributed engineering teams.

Build, operate, and improve data pipelines, backend services, APIs, infrastructure, and analytics platforms supporting warehouse automation systems. Responsibilities include Terraform-based infrastructure, Airflow optimization, BigQuery cost efficiency, monitoring, incident response, CI/CD, and maintaining a customer-facing slot-availability application. The role requires collaboration across distributed engineering teams and work across application code, data pipelines, and cloud infrastructure.

Design, build, and maintain scalable data pipelines, models, and backend data infrastructure. Develop dbt transformation workflows on Snowflake or Databricks, manage orchestration, and create internal data applications with tools such as Streamlit. Translate product requirements into technical solutions, collaborate across engineering and analytics teams, and use GenAI tools to accelerate development from design through deployment and continuous improvement.

Develop and maintain C++ software for data logging, visualization, and processing on Linux platforms. Design and execute software tests, troubleshoot defects, prepare technical documentation, support deployed tools, and collaborate with software, hardware, and systems integration teams. The role also involves implementing standards-compliant solutions, resolving project technical issues, and continuously improving technical expertise.

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.

Designs and operates batch and streaming data pipelines on Databricks and Spark for fintech lending use cases. Owns feature-store reliability, point-in-time correctness, data modeling, governance, observability, incident response, and data-quality controls. Develops agentic workflows with tools, evaluations, state, and guardrails around feature creation and monitoring. Requires strong Python, SQL, Spark, lakehouse, orchestration, and financial data engineering expertise.

Design and own the feature-store architecture supporting credit decisioning, including schemas, point-in-time correctness, freshness SLAs, lineage, data quality, observability, and drift monitoring. Build reference data-engineering patterns and lead complex cross-domain initiatives in regulated environments. Architect agentic harnesses and loop or graph workflows with reliable context, tools, evaluations, guardrails, and cost controls. Serve as a hands-on technical authority who establishes standards and mentors through influence.

Lead data engineering for Finance by designing dbt dimensional models, building Python or Go ETL pipelines, and loading data into BigQuery on Google Cloud. Own data quality, reconciliation, observability, documentation, and production reliability. Lead multi-sprint initiatives, partner with Finance and BI stakeholders, review code, mentor engineers, and use AI-assisted development tools responsibly. The role focuses on trusted financial reporting data, including billing, subscriptions, payments, revenue, and related metrics.

Build secure production services on Google Cloud from prototypes, develop Python APIs and data pipelines, integrate business systems with canonical data layers, automate reporting, and own deployment, monitoring, security, documentation, and stakeholder communication. The role requires strong cloud, data engineering, full-stack, and service ownership skills, with emphasis on autonomous delivery and secure software practices.