Top Data Engineer Jobs in Krakow

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

Build a greenfield data platform and ingestion pipelines for payments, marketing, and operational data across AWS and GCP; implement a Lakehouse with medallion-layer transformations in dbt/SQLMesh (Redshift/BigQuery); own orchestration (Dagster/Airflow), lineage, backfills, retries, alerting; apply IaC with Terraform and cost controls; operate autonomously in a remote, async environment.

Design, build, and maintain scalable AWS-based data platforms and pipelines ingesting from DynamoDB, Aurora PostgreSQL, and Neptune; implement Iceberg-based S3 data lake zones; develop CDC and batch ingestion, Airflow orchestration, Glue Data Catalog integration, CI/CD, automated testing, and monitoring; optimize Iceberg table design and troubleshoot production issues.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design and govern ontologies, schemas, and data models for large-scale life sciences data; validate mappings, translate R&D science into data products, align standards with external ontologies, and document decisions to support data productization and analytics.

Design and operate scalable, near-real-time data pipelines and backend services for a global payments platform. Model transactional ledger data for reconciliation, ensure data quality and observability, and own systems end-to-end in a remote-first environment.

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.

Lead a time‑bounded migration of stateful data systems (Druid, Spark, Redpanda, Airflow, Postgres, Elasticsearch) from bare‑metal VMs to Kubernetes on OpenStack. Codify infrastructure as code (Terraform, Helm/Kustomize, GitOps), operate and tune production data clusters, build observability and SLOs, and own runbooks, KPIs, and incident response for the data platform.

Design and build analytics-ready datasets, enable self-service semantic layers, translate business questions into metrics, ensure data accuracy and freshness, implement testing and CI/CD for reporting and dashboards across Danaher operating companies.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

The Staff Data Engineer will manage product analytics architecture, drive data modeling standards, design data systems, and provide technical leadership across teams to improve data insights and product capabilities.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

The Senior Data Science Engineer will architect software solutions, drive AI research, design AI systems, and mentor junior engineers while focusing on innovative solutions in the travel domain.

Lead technical delivery of data-driven solutions across the data lake and data center. Design, build, and document full-stack data systems, apply Master Data Management, improve data models, mentor engineers, collaborate with business stakeholders, and drive long-term data infrastructure strategy and operational efficiency.

Design, build, and maintain ETL/ELT pipelines and semantic data models in Microsoft Fabric and Azure. Develop complex SQL solutions and DAX logic for Power BI reporting. Implement CI/CD and DevOps practices, monitor and optimize pipeline performance, ensure data quality and governance, and collaborate with international stakeholders on data architecture and analytics solutions.

Design and build data architectures and pipelines, optimize data management, collaborate with AI teams, and implement best engineering practices.

Design and build high-volume batch and streaming data ingestion and curation platforms on AWS and GCP. Lead architecture discussions, mentor junior engineers, troubleshoot ETL/data quality and backfills, and deliver production services with monitoring, CI/CD, and tight SLAs while collaborating with cross-functional teams.

Design and provision AWS lakehouse foundations, implement tenant-isolation security and ingestion pipelines, build Terraform IaC and GitHub Actions CI/CD, orchestrate workflows with Airflow/Astronomer, ensure data lineage/observability and cost governance, and collaborate with architects and engineers while transferring knowledge to client teams.

Owner of all PostgreSQL databases underpinning a multi-site telecom platform. Responsibilities include DB installation, HA, backup/restore, performance tuning, large-scale bulk loads/migrations, multi-datacenter replication, data governance (retention, masking, archival), monitoring (Prometheus/Grafana), automation scripting, and cross-platform integration with network-element databases.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.