Overview:

Come help us transform Waters Corporation (NYSE: WAT)

The Transformation team at Waters Corporation is driving the next phase of enterprise evolution. Our mission is to accelerate integration, execution, and value capture while transforming the capabilities that power our business. As part of our transformation journey, we are building the data foundations required for both integration and advancing technologies, including Agentic AI. These capabilities support data‑driven decisions across the enterprise, enabling better insights, improved processes, and more efficient operations.

This is a unique opportunity to join a team at the center of Waters’ transformation, working on high‑impact initiatives that shape the future of our enterprise. As a Principal Data Engineer, you will be a senior technical contributor who partners closely with data, analytics, and product teams. You will bring deep technical expertise, operate with a high degree of independence, and contribute to the design and delivery of scalable data solutions while mentoring others and influencing best practices.

Responsibilities:

The Principal Data Engineer designs, builds, and enhances robust data pipelines and platforms that support analytics, machine learning, and operational monitoring. This role is primarily hands‑on, with opportunities to contribute to system design, code quality, and cross‑team collaboration. You will be a key problem solver for complex data challenges and a trusted partner to both technical and business stakeholders.

Key Responsibilities

Data Engineering

• Design and build scalable, reliable data pipelines that ingest, validate, transform, and persist structured and semi‑structured data, including large data frames and matrices.
• Contribute to data platform and pipeline design for analytics, machine learning, and Agentic AI workloads, with a focus on performance, reliability, and maintainability.
• Implement and maintain data quality checks, validation logic, and monitoring dashboards to ensure data health and trust.

Software Engineering

• Develop production‑grade Python code using strong OOP and functional programming practices.
• Follow and promote sound engineering practices, including modular design, testing, readability, and maintainability.
• Participate actively in code reviews, automated testing, and source control workflows.

Analytics & ML Enablement

• Partner with Data Scientists and Analysts to ensure data is model‑ready, reproducible, and well understood.