Mission
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.
Profil
Basic Requirements
Education: Bachelor of Science degree in Engineering from an accredited program.
Big Data Ecosystems: Hands-on experience with Snowflake or Databricks.
Programming Languages: Proficiency in SQL, Python, and/or PySpark.
Data Modeling & BI Tools: Proven expertise in data modeling and tools such as Power BI, SharePoint, PowerApps, Power Automate, Azure, or Palantir Foundry.
Cloud Stack Proficiency: Hands-on experience with modern cloud architectures, including Microsoft Fabric (Lakehouse, DirectLake, DAX, Dataflows) and/or AWS cloud services.
Leadership & Soft Skills: Demonstrated leadership, self-starter mindset, organizational skills, and fluent English communication skills with the ability to present to all levels of management.
Preferred Qualifications
Advanced Degree: Master's degree in Data Science, Business Analytics, Computer Science, Information Technology, or a related field.
Domain Experience: Experience supporting core engineering processes (e.g., Sourcing, Quality, Validation, Manufacturing, Logistics, Program Management).
AI/ML Integration: Experience integrating predictive models or AI capabilities into production data pipelines.
Business Analysis: Proven ability in business process mapping, writing Business Requirements Documents (BRDs), and technical process documentation.
Big Data Frameworks: Familiarity with open-source frameworks like Apache Spark or Hadoop.