Data Governance Quality Lead

Lam Research•Fremont, CA

1d•Hybrid

About The Position

The Data Governance Quality Lead is an experienced, business-facing role responsible for executing enterprise data governance and data quality capabilities across core business domains. This role ensures that data quality standards are embedded into operational systems, analytics platforms, and data products to enable trusted decision-making and scalable data consumption. This position drives the definition and implementation of data quality rules, metrics, and controls, working closely with business stakeholders, SAP teams, and data quality engineering teams to proactively prevent downstream data issues within a highly complex, cloud-centric data ecosystem.

Requirements

Bachelor’s degree in business, Data Science, Mathematics, Statistics, or a related field.
8+ years of experience in data quality, data governance, and/or master data management within complex enterprise environments.
Proven experience designing and implementing data quality frameworks, including business rules, metrics, and monitoring controls.
Strong working knowledge of data governance platforms such as Informatica (Axon/CDGC), Collibra, Alation, or equivalent tools.
Solid understanding of SAP core and master data models and enterprise data domains (e.g., customer, product, supplier, finance).
Working knowledge of relational database technologies (e.g., SQL Server, Oracle, Teradata) and data querying concepts.
Familiarity with cloud data environments and modern data platforms, including data lakes, analytics platforms, and integration patterns.
Strong analytical, problem‑solving, communication, and stakeholder management skills, with the ability to influence technical and business teams.

Nice To Haves

Experience working in high‑growth, large‑scale, or highly complex enterprise environments, preferably within High‑Tech Manufacturing, Semiconductor, or related industries.
Hands‑on experience with SAP S/4HANA or other ERP platforms.
Exposure to analytics, AI/ML use cases, or data product development.
Demonstrated experience partnering closely with data engineering, platform, and architecture teams to deliver enterprise data solutions.

Responsibilities

Design, implement, and operationalize enterprise data quality processes aligned with data governance standards and the enterprise digital transformation roadmap.
Own the end-to-end lifecycle of business data quality rules, including definition, validation, thresholds, metrics, and ongoing monitoring.
Compile, curate, and maintain governed data assets—including Critical Data Elements (CDEs), governed attributes, and data quality rules—within data governance platforms, ensuring inventories remain current and revisions are consistently managed.
Leverage Informatica MDM, CDGC, and SILK architecture to execute data governance and quality monitoring; identify and recommend additional tools, services, or capabilities to address data measurement and reporting gaps.
Partner with business stakeholders to translate business requirements into functional and technical data quality specifications.
Facilitate system and data flow mapping within the data governance platform, including documentation of data lineage, system interfaces, and associated data policies.
Lead proactive initiatives to prevent data quality issues from propagating to downstream systems, reporting, and analytics environments.
Define, track, and communicate data quality KPIs, scorecards, issue trends, and remediation progress to stakeholders and governance forums.
Perform data analysis on large and complex data sets to identify trends, root causes of data quality issues, and continuous improvement opportunities.
Define and evolve the enterprise data quality operating model in partnership with the Data Governance Office, ensuring alignment with organizational strategy and governance objectives.
Mentor data stewards and domain leads on data quality standards, tooling, roles, and best practices to drive consistent adoption across data domains.
Provide thought leadership, frameworks, and best practices for the effective use of data management and governance tools to deliver scalable, sustainable data governance and data quality solutions.