Data Engineering Technical Supervisor (Medior) – Databricks & Python

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.

Skills
Python, Databricks, Git, AWS, Azure, GCP, CI/CD, SQL, Automation Testing, Data Modeling