About the role
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
Thermo Fisher Scientific
Data Engineering Technical Supervisor
Position Summary
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.
As part of the Data & Analytics team, the Technical Supervisor will collaborate with data engineers, BI developers, analysts, product owners, and business stakeholders while supporting the reliability, maintainability, and operational continuity of production ETL/ELT processes in Databricks.
Key Responsibilities
* 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.
Delivery & Stakeholder Collaboration
* Partner with BI developers, analysts, product owners, and business stakeholders to translate data requirements into scalable technical solutions.
* Provide technical estimates, identify dependencies and risks, and support prioritization and delivery of engineering initiatives.
* Communicate technical concepts, trade-offs, risks, and recommendations clearly to technical and non-technical stakeholders.
* Ensure solutions meet agreed functional, performance, security, quality, and operational requirements.
Required Qualifications
* 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.
Preferred Qualifications
* Experience with Databricks on AWS/GCP/Azure
* Experience with Unity Catalog and Databricks governance capabilities.
* Experience with data observability, monitoring, and pipeline automation.
* Experience optimizing Spark/Databricks workloads for performance and cost.