What are the responsibilities and job description for the Supply Chain Data Analyst position at Ingenworks?
Role: Supply Chain Data Analyst
Location: Sunnyvale, CA (Hybrid - 3 days)
Duration: 12 months (Extendable)
Roles & Responsibilities:
Data Validation & Analysis
- Validate and cleanse internal BOM (Bill of Materials) and MPN (Manufacturer Part Number) data across systems
- Cross-reference supplier data against external sources (Z2Data, DigiKey, SiliconExpert) to identify gaps, risks, and inconsistencies
- Build automated data quality checks and exception workflows
- Deliver ad-hoc analyses, e.g., "which components in this program have lifecycle risk?" or "where are we single-sourced on long-lead parts?"
Internal Tools & Applications
- Build and iterate on custom internal tools for supply chain risk monitoring (Python, web-based dashboards, APIs)
- Integrate external market intelligence platforms into internal workflows
- Develop AI-assisted features - risk scoring, lifecycle classification, early warning signals
- Create self-service analytics and visualizations for non-technical sourcing leads
Solution Design
- Translate supply chain business problems into technical solutions
- Prototype fast - get a working tool in front of users within days, not months
- Work with existing data infrastructure (pipelines, tables, etc) rather than building from scratch
- Identify opportunities to automate manual processes with AI/LLM tools
Required Skills
- 5 years in a technical analyst, solutions engineer, or applied data science role
- Strong Python - scripting, data manipulation (pandas), API integrations, light web development
- Proficient in SQL — can query large datasets, write complex joins, work with partitioned tables
- Experience connecting to and working with external APIs and data sources
- Ability to build working internal tools quickly - web apps, dashboards, notebooks, scripts
- Comfortable working with messy, real-world data - reconciling across sources, handling edge cases
- Strong communication — can present findings to non-technical stakeholders clearly
Nice to Have
- Supply chain, procurement, or hardware operations background
- Familiarity with electronic component data (BOMs, AVLs, MPNs, lifecycle stages)
- Experience with component databases (DigiKey, Octopart, Z2Data, SiliconExpert)
- Comfort with AI/ML tools - LLMs, classification models, or using AI assistants to accelerate work
- Web frameworks (Flask, Streamlit, React) for rapid prototyping
- Meta internal tools (Bento, Presto/Hive, Unidash, Dataswarm) or equivalent at scale