{"schemaVersion":"jobsearcher.job.v1","id":"d35c298d2b31db69ce41ebc6","url":"https://jobsearcher.com/jobs/d35c298d2b31db69ce41ebc6","canonicalUrl":"https://jobsearcher.com/jobs/d35c298d2b31db69ce41ebc6","title":"Data Quality Lead","description":"Please share resumes at Akanksha@mcubesoft.comData Quality LeadLocation: Cupertino, CA (Hybrid 3 days On-Site and 2 days Remote)Duration: 12 MonthsJob DescriptionKey Responsibilities:Lead business data architecture efforts within Legal Operations in collaboration with cross-functional stakeholders.Define and maintain canonical data definitions and standards across legal, billing, vendor, litigation, patent, contract, and compliance domains.Partner with Legal Ops leadership to implement scalable data governance practices that align with enterprise data strategies.Conduct current-state assessments and design optimized, future-state data flows that support transformation goals.Identify and resolve data quality and integrity issues through root-cause analysis and hands-on remediation, leveraging AI tools where appropriate.Shape and execute a strategy for making business data AI-ready, including proper labeling, enrichment, and structuring for downstream analytics and ML use cases.Translate complex business needs into detailed technical and operational data requirements.Engage confidently with both business and technical stakeholders to drive alignment and adoption.Key Qualifications:10+ years of experience in business data analysis, data architecture, or related fields.Proven success in transformation initiatives or large-scale enterprise change programs.Deep understanding of enterprise data management, governance, and quality frameworks.Experience with Salesforce, Snowflake, and Tableau to support reporting, data modeling, and analytics needs.Strong SQL skills, with working knowledge of data profiling tools and techniques.Exposure to machine learning (ML) concepts and best practices for structuring data to support AI use cases.Experience using or guiding the use of AI/ML technologies for data remediation, categorization, or enrichment.Excellent facilitation and documentation skills; 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