Data Governance Lead

Nexiva•Sunnyvale, CA

20h•Onsite

About The Position

Senior Data Governance Lead for a high-tech company, supporting a supply chain center of excellence and hardware supply chain organization. The role is full-time and onsite Monday through Friday in Sunnyvale, California, on a 12-month contract with a possible extension. The final interview round must be attended in person. The position focuses on building and governing trusted supply-chain data in Databricks, improving data quality, standardizing metrics, and preparing data for AI-enabled use. Strong supply-chain experience, particularly in consumer hardware, is required. The posting excludes candidates with finance or healthcare experience and requests a two- to three-page resume. The Data Governance Lead owns data-quality alerting and issue management from detection through resolution, including alert coverage, triage, routing, service-level agreements, root-cause analysis, and closure. The role ensures quality and abnormality checks remain current as pipelines and models change, and reports data-quality health and recurring failure patterns to stakeholders. The role also owns metric definitions, calculations, terminology, and measurement standards across functions; establishes certification standards and governance gates for metrics used in official reporting; and documents and maintains standards in an internal tool. In collaboration with Analytics Engineers, the lead helps architect and govern the Databricks Gold layer, including certified data models with embedded quality and abnormality checks, metrics views, and data lineage. The position also contributes to tiered data-readiness assessments for AI and translates certified metrics into natural-language-queryable experiences. The original job description further outlines responsibility for KPI taxonomy and enterprise performance data modeling; master data governance across product, supplier, customer, and compliance domains; and cross-functional alignment on reporting standards and measurement methodologies. The lead serves as a governance subject-matter expert in architecture reviews and mentors analysts, data engineers, and BI developers.

Requirements

Strong supply-chain background, including consumer hardware experience.
Strong background in data governance, data management, or data architecture.
Experience with Databricks, Unity Catalog, data lineage, and enterprise data platforms.
Expertise in KPI framework design, metric governance, and data modeling.
Experience establishing master data governance programs and enterprise data standards.
Knowledge of AI/ML data readiness, data-quality assessment frameworks, and analytics platforms.
Ability to influence cross-functional stakeholders in a matrixed environment without direct authority.
Strong communication, leadership, and mentoring skills.
Must work onsite Monday through Friday in Sunnyvale, California.
Must attend the final interview round in person.
Resume must be two to three pages.
Finance or healthcare experience is not accepted for this role.
LinkedIn profile must have a professional picture and match the CV.
Candidates must be able to work on a 1099 or C2C basis; the posting states H-1B visa support is not provided.

Responsibilities

Own detection-to-resolution for data-quality and abnormality issues in Databricks, including alert coverage, triage, routing, SLAs, root-cause analysis, and closure.
Ensure engineers maintain data-quality and abnormality checks as pipelines and models change, including standards, review cadence, and prevention of silent drift on new or modified tables.
Report data-quality health and recurring failure patterns to stakeholders.
Define and maintain metric definitions, calculations, terminology, and measurement standards across functions.
Establish metric certification standards and governance gates for metrics included in official reporting.
Document and maintain metric standards in the internal tool, and mentor analysts, data engineers, and BI developers in applying them.
Work with Analytics Engineers to architect and govern the Databricks Gold layer, including certified data models, embedded data-quality and abnormality checks, metrics views, and data lineage.
Develop or contribute to tiered assessments of dataset completeness, accuracy, governance maturity, and automation readiness to determine appropriate levels of AI autonomy.
Translate certified metrics into natural-language-queryable experiences.
Own and maintain KPI taxonomy, connecting executive-level metrics with operational drivers and root-cause indicators.
Develop and govern the enterprise performance data model, including standardized definitions, time grains, dimensional hierarchies, and reporting structures across supply-chain domains.
Lead master data governance across product, supplier, customer, and compliance domains; establish standards, policies, and processes to improve data consistency and quality.
Develop standardized KPI calculation frameworks, including actuals versus targets, rolling averages, period-over-period comparisons, and statistical process control methodologies.
Establish target and threshold management processes that support cascading business objectives, seasonal adjustments, and planning updates.
Lead data-readiness-for-AI initiatives and enable natural-language access to certified metrics using platforms such as ThoughtSpot, AtScale, Power BI, Looker, Tableau, or similar technologies.
Drive alignment on metric definitions, reporting standards, and measurement methodologies across business and technology teams.
Serve as a data governance subject-matter expert in architecture reviews and mentor analysts, data engineers, and BI developers.
Attend the final interview round in person.