What are the responsibilities and job description for the Data Governance Analyst position at Signature IT World Inc?
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)