Data analyst/Data Engineer

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, Direct. Lake) using SQL, Python, Py. Spark, 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 , Power. Apps , 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.

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 Py. Spark . Data Modeling & BI Tools: Proven expertise in data modeling and tools such as Power BI , SharePoint , Power.

Apps , Power Automate , Azure , or Palantir Foundry . Cloud Stack Proficiency: Hands-on experience with modern cloud architectures, including Microsoft Fabric (Lakehouse, Direct. Lake, 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

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.