Principal Data Engineer
Role details
Job location
Tech stack
Job description
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 endtoend data engineering - from ingestion and modeling through automation and governance - with responsibility for enabling highquality analytics. The engineer will own the delivery of analytics data assets and also deliver select productiongrade report development in Power BI, ensuring that strategic dashboards are reliable, performant, and widely adopted., 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 enterprisegrade 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).
- Power BI Reporting
- Engineer performant Power BI datasets, dataflows, and semantic models; define and maintain measures (DAX), calculation logic, and rowlevel 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 selfservice by publishing certified datasets and patterns; review community reports for conformance and reliability.
- Process Intelligence, Automation & Cross Functional Collaboration
- Deliver integrated data models for process mining and operational analytics (e.g., LeadOpportunityOrderInvoice).
- 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.
Requirements
- 7+ years in data engineering, data architecture, or analytics engineering roles.
- Expertlevel SQL and Python with a strong understanding of data warehousing principles and dimensional modeling.
- Handson 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.
Preferred Qualifications
- Experience in Commercial Excellence, Revenue Operations, Sales Operations, or Finance
- Familiarity with process mining (Celonis) and eventbased data models.
- DevOps for data: Git, CI/CD, environment promotion, automated testing.
- Experience with privacy/compliance and secure data design (RLS, PII handling).