Data Governance Analyst

Role: Data Steward/Data Scientist & Reporting Analyst

Location: Cupertino, CA

Type: Contract

Job Summary:

We are looking for a rare blend of analytical depth, data ownership mindset, and modern AI fluency. As a senior data steward & scientist you will sit at the intersection of advance analytics, knowledge representation, data governance and intelligent automation - helping us unlock the full value of our data assets whole ensuring data is trusted, well understood and for purpose.

You will not just analyze data - you will govern the definitions that describe it, and deploy AI agents that automate decision on top of it.

Key Responsibilities:

Data Science & Analytics:

Have familiarity with designing, developing and deploying statistical models, machine learning algorithms, and analytical framework to support channel business decisions

Have hands on experience in conducting exploratory data analysis to surface insights on sales performance, inventory

Have familiarity with building an maintaining forecasting models

Translate complex analytical outputs in clear, actionable recommendations for business audience

Data Governance & Metric Definition:

Serve as the data steward for key data domains owning definitions, lineage and quality standards

Develop an maintain data dictionaries, business glossaries and lineage documentation

Define and monitor data quality riles, triage and resolve data issues in partnership with engineering

Ensure AI outputs and agent generated content are traceable back to governed, trusted data sources

Design, build and deploy AI agents that automate recurring analytical workflow in data governance

What You’ll bring

Required

5+ years of experience across data science, data engineering, or analytics — with at least 2 years in a retail or consumer goods environment

Strong proficiency in SQL and Python (pandas, scikit-learn, statsmodels, or similar)

Familiarity with causal inference methods and experiment design

Familiarity with building and maintaining data pipelines (Airflow, dbt, Spark, or similar)

Practical experience building or deploying AI agents or LLM-powered applications

Familiarity with knowledge graph technologies (RDF, property graphs, Neo4j)

Experience with data governance practices — data quality, metadata management, or data stewardship

Ability to communicate complex findings clearly to non-technical stakeholders

Nice to have:

Experience with multi-agent frameworks (Claude Agent SDK, LangGraph, CrewAI, or similar)

Exposure to ontology design or entity resolution in a retail context

Familiarity with data governance frameworks (DAMA-DMBOK or simila

Graph query languages (SPARQL, Cypher)

Cloud data platform experience (Snowflake, BigQuery, Databricks)