Top Data Engineer Jobs in Krakow

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Design and implement scalable data pipelines, ETL and data integration solutions, SQL-based transformations, and analytical data models. Optimize database and ETL performance, translate business requirements into robust data solutions, and collaborate in agile cross-functional teams on cloud, AI, and big-data initiatives. The role supports multiple experience levels, with increasing autonomy and responsibility, and requires travel to client locations across Europe.

Design and build scalable batch and streaming data pipelines using Databricks, Apache Spark, PySpark, Spark SQL, and Delta Lake. Develop Lakehouse and Medallion architecture data models, integrate cloud and enterprise data sources, optimize large-scale SQL workloads, and implement data quality, testing, CI/CD, DevOps, and DataOps practices. Collaborate with architects, analysts, and data scientists to deliver production-grade data products. The role is hybrid in Poland and requires travel to client locations across Europe.

Build and manage Python-based data ingestion workflows and pipelines, integrating downstream systems such as ServiceNow. Use Snowflake, SQL, and OpenTelemetry to support data storage, validation, optimization, and observability. Troubleshoot pipeline failures, maintain data integrity, and improve workflow automation. Preferred experience includes orchestration tools such as Airflow, Prefect, or Dagster, ITSM and ITIL practices, and CI/CD pipelines for data products.

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.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Own the architecture, ingestion, governance, optimization, retention, and correctness of a large-scale financial data lakehouse. Design bronze, silver, and gold layers; manage CDC streaming through Kafka and Debezium; optimize TB-scale queries; ensure reconciliation, freshness, regulatory retention, GDPR deletion, lineage, access controls, PII masking, and infrastructure cost monitoring.

Own the company data lakehouse supporting millions of financial events daily. Design bronze, silver, and gold architectures; build CDC streaming ingestion; optimize large-scale storage and queries; manage retention, archival, and GDPR deletion; ensure reconciliation, freshness, and data quality; and implement governance, access controls, PII masking, encryption, lineage, and auditability. Monitor pipeline health, anomalies, and cloud costs across S3 and Snowflake or Databricks.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Lead the architecture and scaling of high-throughput data services, ingestion pipelines, and distributed data platforms. Build low-latency, memory-efficient infrastructure in Rust; design resilient APIs, schemas, and data contracts; integrate services with cloud warehouses and lakehouses; and optimize distributed processing, storage, caching, and query performance.

Design, build, and maintain large-scale data processing systems and pipelines (Spark, BigQuery, dbt/Dataform). Improve scalability, performance, and reliability; own projects end-to-end; extend and operate Airflow; work with cloud (GCP/AWS), Kubernetes, and CI/CD practices.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures while integrating multiple data sources. Ensure data quality, scalability, and performance, and collaborate with BI teams supporting Power BI and reporting platforms. Participate in cloud data architecture initiatives under senior guidance.

Develop and maintain scalable AWS data pipelines, modify Lambda functions and SQL processing jobs, design Iceberg-based data models, optimize Athena and Spark workloads, and implement data quality, observability, testing, governance, and documentation practices. The role also requires collaboration with engineering, product, and data stakeholders, along with GitLab-based version control and CI/CD or Infrastructure as Code familiarity.

Design and implement scalable data pipelines, ETL processes, data integration solutions, and analytical data models. Work with GCP services, SQL, dbt, Python, and Data Vault 2.0 in agile, cross-functional AI teams. Support scalable AI/ML initiatives, collaborate with technical and business stakeholders, and travel to client locations across Europe or occasionally beyond as project needs require.

Design, build, and operate GitOps-driven data and AI platforms and pipelines (ETL/ELT, feature stores, batch/real-time inference). Enable GenAI/RAG (embeddings, vector ingestion), implement IaC and CI/CD, enforce data quality, governance, observability and MLOps, and support Azure/AWS-based production deployments while collaborating with security, product, and operations 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.

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.

Lead architecture and technical standards for a greenfield, enterprise-scale data platform supporting analytics, operational products, and AI workloads. Design Iceberg-based event flows, canonical data models, batch and near-real-time pipelines, governance, data quality, observability, tenant isolation, and regional data strategies. Evaluate platform technologies, optimize performance and costs, mentor engineers, and collaborate with leadership and platform teams to deliver a production-ready foundation.

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.

Design, implement, and optimize ETL/data pipelines, data quality metrics, APIs, and enrichment for AI/ML platforms; research big-data technologies; run trials, write technical documentation, and collaborate in project teams.

Develops and maintains Microsoft Lists, Power Automate flows, Excel-based analysis tools, and Power BI semantic models, reports, dashboards, and paginated reports. Responsibilities include automating data processing, designing linked lists, improving data-entry workflows, manipulating multi-source data with Power Query, creating DAX measures, managing relationships, and supporting end-user customization of Power BI visuals. Requires a relevant bachelor’s degree and 3–4 years of experience developing data tools for electronics components.