Data Engineering Technical Supervisor (Medior) – Databricks & Python
Employer · Thermo Fisher Scientific
Software developer
Database administrator
IT managerDaytimeFull-timeBudapest
We are seeking a Data Engineering Technical Supervisor to provide hands-on technical leadership and day-to-day guidance to a team of data engineers building scalable, reliable, and high-quality data solutions. This role does not include people management responsibilities. The role combines hands-on data engineering, technical supervision, mentoring, and project leadership, with a strong focus on Databricks, Python/PySpark, SQL, Delta Lake, and modern data engineering practices.
Tasks
- Provide technical leadership and mentoring to data engineers and technical oversight for Databricks solutions.
- Lead and contribute hands-on to the design and implementation of scalable ETL/ELT pipelines using Databricks, Python/PySpark, SQL, and Delta Lake.
- Guide engineers on architecture, design patterns, coding standards, testing, and performance optimization.
- Review technical designs and code to ensure solutions are scalable, maintainable, secure, and aligned with engineering standards.
- Troubleshoot complex production issues, support root-cause analysis, reduce technical debt, and improve platform reliability.
- Promote engineering best practices including Git-based source control, automated testing, CI/CD, peer review, and documentation.
- Develop reusable Python/PySpark components and standardized patterns for data ingestion, transformation, validation, and processing.
- Work with Delta Lake, Unity Catalog, and lakehouse architecture patterns, and support Databricks workflow orchestration and automation.
- Implement appropriate data quality, monitoring, observability, and error-handling mechanisms.
- Enable BI developers to build ETL/ELT pipelines within governed engineering patterns, including approved templates, standards, guardrails, and code reviews.
- Facilitate technical discussions, design reviews, knowledge sharing, and technical onboarding.
- Foster a collaborative engineering culture focused on ownership, continuous improvement, and high-quality delivery.
Requirements
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or a related discipline, or equivalent professional experience.
- 4 years of professional experience in data engineering, software engineering, or a related technical field.
- Strong hands-on experience with Python/PySpark, Databricks, Apache Spark, and SQL in production data engineering.
- Experience designing and operating production-grade ETL/ELT pipelines using Delta Lake and lakehouse architecture concepts.
- Solid understanding of data modeling, data quality, partitioning, performance optimization, and distributed data processing.
- Experience with Git-based development, automated testing, CI/CD, and code review.
- Demonstrated ability to troubleshoot complex technical issues and provide constructive engineering guidance.
- Experience mentoring engineers or providing technical leadership.
- Strong analytical, problem-solving, communication, and stakeholder-management skills.