Manager, IT Data Engineer

Work Schedule

Environmental Conditions

Job Description

Thermo Fisher Scientific Inc. is the world leader in serving science, with revenues of more than $40 billion and approximately 1,20,000 employees globally. Our Mission is to enable our customers to make the world healthier, cleaner, and safer. We help our customers accelerate life sciences research, solve complex analytical challenges, improve patient diagnostics, deliver medicines to market and increase laboratory productivity.

About Team: We are Automation, AI and Data (AAD) team that caters to data engineering and analytics, automation and AI solutions for various groups and divisions within Thermofisher Scientific.

What will you do?

As an engineering Manager, you will play a key role in strengthening our data engineering capabilities delivering data engineering pipelines and solutions through Enterprise Data Platform (EDP) for various groups and divisions.

• Collaborate with functional analysts to convert business requirements into scalable data engineering and GenAI-ready pipelines.
• Collaborate with the Scrum Master on product backlogs and support sprint planning and delivery.
• Build, test, and optimize data pipelines and feature stores for various use cases, including real-time, batch processing, and Generative AI/ML workloads.
• Support the evolution of Enterprise Data Platform (EDP) architecture and participate in roadmap activities related to data, analytics, and AI platform initiatives.
• Collaborate with leadership and partners to ensure high-quality, governed data across DWH, AWS, and AI/ML consumption layers for BI, analytics, and GenAI use cases.
• Provide hands-on technical guidance and oversight across multiple data and AI-driven projects.
• Identify potential risks related to data quality, model inputs, and AI outputs, and work with partners to define mitigation strategies.
• Actively support and contribute to development activities in data engineering and GenAI enablement, ensuring bandwidth and technical direction when needed.
• Implement and follow Agile development methodologies, adhering to DevOps, DataOps, and MLOps/LLMOps best practices.
• Ensure teams follow prescribed development processes, coding standards, and architectural patterns.

• 10+ years of overall experience with 7+ years focused on delivering enterprise-scale data engineering solutions.
• 5+ years of proven experience building Cloud-based data and BI solutions on AWS.
• Strong hands-on experience with data modeling, data pipelines, and large-scale distributed data processing.
• 5+ years of programming experience in SQL, PySpark, and Python.
• Experience working with Generative AI or ML-enabled solutions, including:
• Preparing, curating, and optimizing data for LLMs and ML models
• Supporting RAG (Retrieval-Augmented Generation) patterns, embeddings, vector stores, or feature stores
• Familiarity with AI/ML lifecycle practices, including MLOps/LLMOps, model integration, and production deployment considerations.
• Experience with Agile delivery models, following DevOps, DataOps, and DevSecOps practices.
• Excellent written, verbal, interpersonal, and partner communication skills.
• Strong analytical skills with the ability to translate business requirements into data and AI-ready technical solutions.
• Ability to work effectively with cross-functional, globally distributed teams using multiple communication channels (Email, MS Teams, meetings).
• Excellent prioritization, problem-solving, and decision-making skills.

Our Mission is to enable our customers to make the world healthier, cleaner, and safer. Watch as our colleagues explain 5 reasons to work with us. As one team of 1,20,000 colleagues, we share a common set of values - Integrity, Intensity, Innovation and Involvement - working together to accelerate research, solve complex scientific challenges, drive technological innovation and support patients in need.

Thermo Fisher Scientific is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status.