Freelancer Data Engineer (PowerBI Support and Knowledge Transfer on Databricks) m/w/d
This job offer is no longer available.
München, Deutschland |
Freelance
We are looking for an experienced Data Engineering and Business Intelligence Consultant to support the industrialization and scaling of local data pipelines and analytical solutions in a Databricks environment on AWS.
The project combines data engineering, MLOps, data modeling, Power BI development, quality assurance, and knowledge transfer. The consultant will transform existing proof-of-concept work into robust, production-ready pipelines and make the resulting insights accessible through reliable and intuitive Power BI reporting.
The role is highly hands-on and requires someone who can work across the full delivery lifecycle—from reviewing existing Python notebooks and designing data models to deploying production components, documenting the solution, and enabling the internal team to take ownership.
Zu besetzendes Projekt: Freelancer Data Engineer (PowerBI Support and Knowledge Transfer on Databricks) m/w/d
Branche: Biotech
Fähigkeiten: Data Engineer,PowerBI
Projektstart: 01.09.2026
Projektdauer: 30.12.2026
Einsatzort: München, Deutschland
Project goals
The main objectives are to:
- Integrate new data sources into the existing pipeline architecture.
- Industrialize existing proof-of-concept solutions for production use.
- Build scalable, well-structured data preparation layers for Power BI.
- Improve the performance and reliability of reporting data models.
- Establish data-quality controls and monitoring mechanisms.
- Strengthen metadata governance and data cataloging in Databricks.
- Enhance existing Power BI reports and translate analytical results into clear business insights.
- Transfer knowledge and best practices to the internal Data Science and Data Engineering teams.
Key responsibilities
Data engineering and pipeline development
- Design, develop, and implement optimized curated schemas.
- Integrate additional data sources into the existing architecture.
- Refactor Python notebooks and implement proof-of-concept solutions within production pipeline structures.
- Develop robust pipeline orchestration and scheduling jobs.
- Manage the production release of developed components.
MLOps and industrialization
- Configure MLflow for model tracking and lifecycle management.
- Apply production-grade deployment principles to machine-learning-related components.
- Integrate proof-of-concept outputs into the core reporting data model.
- Ensure developed solutions are maintainable, testable, and scalable.
Data modeling and Power BI
- Architect and build data preparation layers optimized for Power BI ingestion.
- Design efficient reporting data models and curated schemas.
- Tune query performance and optimize models for speed and scalability.
- Design and deploy advanced visualizations within the existing Power BI environment.
- Configure and test Row-Level Security to ensure secure access to data.
- Present complex analytical results through intuitive dashboards for business stakeholders.
Quality, governance, and documentation
- Implement testing protocols to detect data-quality issues and data drift.
- Safeguard pipelines against unexpected or invalid data ingestion.
- Maintain metadata in the Databricks data catalog.
- Apply appropriate data-sensitivity classifications.
- Document the data model, architecture, implementation details, and example queries.
- Maintain technical documentation in Confluence and Markdown-based code documentation in the repositories.
Collaboration and knowledge transfer
- Plan and track work using Jira.
- Participate in internal reviews to ensure quality and alignment.
- Collaborate closely with Data Scientists, Data Engineers, project leadership, and business stakeholders.
- Educate the internal team on implementation details, best practices, and operational procedures.
- Support the long-term self-sufficiency and ownership of the internal teams.
Ideal consultant profile
The ideal person is a senior, hands-on practitioner who combines strong software engineering fundamentals with practical experience in analytics delivery. They are comfortable moving between code, data architecture, production operations, dashboards, and stakeholder communication.
The consultant should bring:
- Several years of professional Python development experience.
- Strong practical experience with Databricks on AWS.
- Advanced PySpark skills for large-scale data processing.
- Deep knowledge of modern data warehousing and data modeling principles.
- Proven experience designing curated schemas and orchestrated data pipelines.
- Strong Power BI expertise, including data modeling, visualization, query optimization, and Row-Level Security.
- Practical MLOps experience, including MLflow and production pipeline orchestration.
- Experience with Git, agile workflows, Jira, and collaborative software development.
- A structured approach to testing, documentation, and data governance.
- The ability to explain complex technical topics clearly and transfer knowledge effectively.
Reference number: 17733
Industry: Biotechnology
Project start: 01.09.2026
Duration: 30.12.2026
Employment type: Freelance
Contact
Luca Furler (DE)