Position Summary The Lead Product Development Data Engineer owns key data processes and analytics applications within the broader Product Development Process (PDP). In this cross-functional role, you will bridge the gap between Engineering and enterprise organizations—including IT, Manufacturing, Finance, Purchasing, and Supply Chain—to establish standardized, high-performing data workflows, modern cloud architectures, and scalable analytics solutions. Key Responsibilities 1.

Data Architecture & Cloud Engineering Enterprise Platform Management:

Build, optimize, and maintain big data pipelines and architectures leveraging Snowflake , Databricks , or Microsoft Fabric .

Cloud & Data Pipelines:

Design scalable data models and storage solutions (Lakehouse, DirectLake) using SQL, Python, PySpark, and cloud ecosystems across Azure or AWS.

Modern Analytics Integration:

Migrate legacy data workflows to modern cloud architectures, establishing robust data governance, pipeline security, and data lineage. 2.

Process Governance & Analytics Standards Process Ownership:

Drive global uniformity, documentation, and continuous refinement across assigned Product Development processes and digital applications.

Business Intelligence & Dashboards:

Develop, deploy, and maintain executive-level dashboards and reporting tools using Power BI , PowerApps , and Palantir Foundry .

Workflow Automation:

Streamline administrative and operational cross-functional engineering tasks using Power Automate and Microsoft 365 tools. 3.

Cross-Functional Leadership & AI/ML Support Stakeholder Alignment:

Collaborate across IT, Manufacturing, Finance, Purchasing, and Program Management to capture technical requirements and align business goals.

AI/ML Workflows:

Support the integration of machine learning models and predictive analytics into existing engineering data pipelines.

Executive Communication:

Translate complex data technicalities and process trade-offs into clear decision packages, business cases, and leadership presentations.