Principal Data Engineer

Overview

The Principal Data Engineer will lead the architecture, development, and optimization of scalable data solutions that power insights across Commercial Excellence, and enterprise process initiatives. This role focuses on end‑to‑end data engineering - from ingestion and modeling through automation and governance - with responsibility for enabling high‑quality analytics. The engineer will own the delivery of analytics data assets and also deliver select production‑grade report development in Power BI, ensuring that strategic dashboards are reliable, performant, and widely adopted.

Role Impact

This role will shape the Commercial Excellence data and reporting foundation - enabling trusted, fast, and actionable insights, accelerating enterprise transformations (e.g., S/4HANA, Customer Master redesign), and elevating analytics maturity across Waters.

• Design, develop, and maintain scalable data pipelines across SAP S/4 Salesforce, Marketo, Adobe Analytics, and other core enterprise systems.
• Architect and implement data models in Databricks, Synapse or similar platforms.
• Build ingestion frameworks, transformation logic, and orchestration workflows (e.g., Azure Data Factory, Databricks Jobs).
• Implement data quality, validation, and monitoring frameworks; codify tests and SLAs for critical datasets.
• Lead development of enterprise‑grade semantic models and reusable data assets supporting Sales, Marketing and Finance
• Partner with IT, Master Data, and Process Excellence teams to align data structures with evolving enterprise process designs (Lead to Cash, Demand Gen, Customer Master, Pricing).

• Engineer performant Power BI datasets, dataflows, and semantic models; define and maintain measures (DAX), calculation logic, and row‑level security where required.
• Design and build prioritized enterprise dashboards and paginated reports in Power BI, partnering with business stakeholders on UX, KPIs, and adoption.
• Establish standards for dataset refresh, parameterization, gateway configuration, and workspace governance; resolve performance bottlenecks (model size, query folding, DAX).
• Enable self‑service by publishing certified datasets and patterns; review community reports for conformance and reliability.

• Deliver integrated data models for process mining and operational analytics (e.g., Lead→Opportunity→Order→Invoice).
• Drive alignment on data definitions, lineage, ownership, and governance across stakeholders; document lineage and critical data elements.
• Mentor engineers and analysts; champion code reviews, version control, and deployment automation.

• 7+ years in data engineering, data architecture, or analytics engineering roles.
• Expert‑level SQL and Python with a strong understanding of data warehousing principles and dimensional modeling.
• Hands‑on with Databricks/Spark (PySpark), and/or Azure Synapse; experience orchestrating with ADF or equivalent.
• Proven experience engineering data for Power BI and developing production dashboards (DAX, Power Query, dataflows).
• Experience with SAP, Salesforce, Marketo, or other enterprise operational systems.
• Strong grasp of data quality, lineage, governance frameworks, and master data concepts (customer, product, pricing).
• Ability to partner effectively with technical and business stakeholders in a global matrixed environment.

• Experience in Commercial Excellence, Revenue Operations, Sales Operations, or Finance
• Familiarity with process mining (Celonis) and event‑based data models.
• DevOps for data: Git, CI/CD, environment promotion, automated testing.
• Experience with privacy/compliance and secure data design (RLS, PII handling).