Senior Data Quality & Governance Analyst

Lam Research

Fremont (CA)

On-site

USD 92,000 - 211,000

Full time

14 days+

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Job summary

Lam Research's Global Information Systems Group is seeking a data governance professional to translate data quality standards into scalable technical implementations across the enterprise, embedding validation, MDM survivorship, and metadata management to improve data trust. This role focuses on catalog onboarding, data lineage, and governance-driven data products for enterprise analytics.

The candidate will lead profiling, threshold management, and dashboard development, ensuring high-quality

Qualifications

8+ years of related experience with a Bachelor's in CS, 6+ years with a Master's, or 3 years with a PhD
6–8 years of management experience
6+ years hands-on experience in data quality, data engineering, or governance-focused roles
Strong SQL and data pipeline development experience (ETL, IDMC, SQL-based workflows)
Experience with Informatica CDGC and data catalog tools
Experience with business glossaries, data lineage, and governed metadata management
Strong analytical skills with RCA experience

Responsibilities

Translate business-defined data quality rules into technical implementations.
Implement data validation, cleansing, and standardization rules, including MDM survivorship logic.
Enable execution of data quality frameworks, including rule standardization, threshold management, and alignment to CDEs.
Perform data profiling and validation, including null checks, pattern analysis, duplicate detection, and anomaly identification.
Configure and support CDGC capabilities, including data catalog onboarding and metadata ingestion.
Enable business glossary mapping, CDE classification, and data product onboarding into the marketplace.
Support and validate data lineage across systems to ensure traceability and impact analysis.
Embed data quality checks and pass/fail gates into data pipelines (IDMC, ETL, and SQL).
Monitor data quality scorecards, track defects, and collaborate on issue classification and prioritization.
Perform root cause analysis (RCA), validate fixes through regression testing, and recommend structural improvements.
Develop and support data quality dashboards.