Top Data Engineer Jobs in Poland

The Senior Data Science Engineer will develop and deploy machine learning solutions focused on demand forecasting and optimization for a fleet management platform, ensuring reliability and impact, while working with various stakeholders.

As a Senior Data Engineer, you will architect intelligent data ecosystems, build ETL pipelines, mentor team members, and optimize data processes using AI technologies.

The Senior Data Engineer will design and maintain data models and pipelines, ensure data quality, and collaborate with teams to deliver data solutions using Snowflake and DBT.

Design, implement, and deploy ML and AI solutions; architect AI systems; build and oversee data pipelines; collaborate with engineering teams; translate business needs to data problems; present results to stakeholders; follow Agile practices.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

As a Lead Python Engineer, you will build a multitenant data platform and data ingestion pipelines on AWS, collaborating with various teams and improving processes.

As a Senior Principal Software Engineer, you will develop data recovery software, integrating AI capabilities, leading technical decisions, and mentoring junior engineers.

The Staff Data Platform Engineer will build and optimize data pipelines, manage data processing workflows, and ensure scalability and reliability of backend platforms using Python, Spark, and Databricks.

Design and maintain scalable data pipelines, improve data processes, collaborate on algorithms, and ensure reliable delivery of internal data products.

The Full-Stack Developer will build scalable frontend components using Angular, develop RESTful APIs with Java Spring Boot, integrate third-party APIs, and collaborate with other teams to deliver complete features.

The Lead Data Engineer will develop enterprise-level data pipelines, oversee data lifecycle management, and provide technical leadership while ensuring data security and compliance.

Design, develop, and maintain software for asset monitoring solutions. Conduct code reviews, resolve defects, and collaborate within an agile team.

The Lead Data Engineer will architect and maintain data pipelines, optimize SQL Server models, develop Python scripts, and mentor junior engineers, ensuring efficient and scalable data workflows.

The Senior Data Engineer will own end-to-end data pipeline work, maintain production pipelines, integrate new data sources, and collaborate with data scientists.

The role involves managing and transforming high volume data to solve business issues related to fraud and compliance, using big data technologies while working in Agile sprints and collaborating with various stakeholders.

Design, build, and scale Snowflake-based data products and pipelines using Matillion and dbt. Lead cross-team data initiatives, enforce best practices for modeling, testing, CI/CD, observability, and implement automated data quality, onboarding, and optimization for analytics and AI/ML.

Design and build analytics-ready datasets and lakehouse solutions, enable self-service semantic layers and APIs, ensure data accuracy and freshness, implement testing and CI/CD for reporting and dashboards, and partner with stakeholders to translate business questions into metrics that drive measurable outcomes.

Design, build, and maintain scalable Snowflake-based data products and pipelines using Matillion and dbt. Lead complex cross-team data initiatives, set modeling and observability best practices, implement data quality automation, and support cloud data ecosystems for analytics and AI/ML.

Design and build analytics-ready datasets and lakehouse solutions, enable self-service semantic layers and APIs, ensure data quality and freshness, implement testing and CI/CD, and partner with stakeholders to deliver accurate reporting and dashboards.

Lead architecture and hands-on implementation of a cloud-native, distributed enterprise platform. Design scalable data services, APIs, orchestration, and CI/CD/infrastructure-as-code. Drive Kubernetes-based deployments, observability, and developer tooling while mentoring engineers and aligning architecture with business goals.

Lead design and implementation of a cloud-native enterprise platform: architect core services, data flows, APIs, and deployment automation. Build scalable distributed systems, improve observability and CI/CD, and collaborate across product and operations to ensure performance, reliability, and security.

Lead architecture and development of scalable, hybrid/cloud-native data security and SaaS platforms. Drive platform vision, mentor engineers, ensure compliance-ready designs, and standardize CI/CD and DevSecOps practices across products.

Lead design and ownership of on‑prem Kubernetes deployment, installers, packaging, and upgrade workflows. Ensure operability, security, and reliability in enterprise and restricted environments while mentoring engineers and partnering with Support.

Design and implement data-aware full-stack systems: authenticated Next.js/React frontends, FastAPI backends, secure OAuth2/OIDC access, data integrations, PoCs to production, infra-as-code on AWS, and tooling to enable data scientists and analysts.

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.