Harman

Introduction: A Career at HARMAN Automotive

We’re a global, multi-disciplinary team putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career.

• Engineer audio systems and integrated technology platforms that augment the driving experience
• Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence
• Advance in-vehicle infotainment, safety, efficiency, and enjoyment

About the Role

We are seeking an experienced and proactive Data Quality Lead who will be accountable for end‑to‑end enterprise data quality across critical business domains (e.g., Material, Business Partner, Finance, and related master data). This role owns the data quality operating model, rule lifecycle, monitoring cadence, remediation governance, and measurable improvements—ensuring trusted data is available for business operations, analytics, and AI use cases.

As part of HARMAN’s data modernization journey, this role will operationalize Microsoft Purview as the enterprise data catalog and governance platform (mandatory) and Microsoft Fabric as the primary platform for data quality execution, monitoring, and trend analytics. The Data Quality Lead partners with business data stewards and data owners to define quality expectations and drive adoption, and collaborates with Digital teams and DQ developers to implement scalable, automated, and AI‑enabled data quality capabilities.

What You Will Do

End‑to‑End Data Quality Accountability

• Be accountable for enterprise‑wide data quality outcomes, spanning definition, execution, monitoring, remediation, and reporting.
• Establish and govern the complete data quality lifecycle: define → implement → monitor → remediate → sustain.
• Drive Critical Data Element (CDE) quality management, including prioritization, thresholds, scorecards, and performance reporting.
• Translate data quality issues into business impact and risk, and drive resolution through defined governance mechanisms.

Stewardship Enablement & Cadence Governance

• Partner with business data stewards and data owners to define data quality rules, acceptance criteria, thresholds, and exception handling.
• Ensure data quality monitoring and cleansing activities are executed according to agreed cadences (weekly, monthly, quarterly), with clear ownership.
• Implement operational rhythms, including steward reviews, backlog triage, remediation planning, and closure verification.
• Escalate persistent or cross‑functional data quality risks to governance forums (e.g., Data Council) with clear decisions and action requests.

 Technology Enablement (Microsoft Purview – Mandatory)

• Lead the implementation and adoption of Microsoft Purview as the enterprise platform for:
  • Data cataloging, discoverability, and stewardship enablement
  • Ownership, accountability, and governance workflows
  • Lineage visibility and trust signals for certified and verified data
• Ensure data quality transparency is embedded into catalog experiences (e.g., quality status, rule coverage, definitions, and steward contacts).

 Data Quality Execution & Monitoring (Microsoft Fabric)

• Partner with data quality developers and platform teams to implement rule execution and monitoring in Microsoft Fabric.
• Define how data quality results are stored, trended, and surfaced (attribute‑, domain‑, and enterprise‑level rollups; dashboards; scorecards).
• Establish data quality SLAs, trend monitoring, alerting, and action tracking for red/amber conditions.
• Ensure quality controls are embedded early in data pipelines and data products (shift‑left quality).

 Automated Remediation & Issue Management

• Design and operationalize automated remediation workflows so data quality issues are:
  • Automatically logged (e.g., ticket or work‑item creation)
  • Routed to the appropriate owner, steward, or system team
  • Prioritized based on business impact and severity
  • Tracked end‑to‑end with full auditability from detection to closure
• Define remediation KPIs (cycle time, recurrence, backlog burn‑down, verification pass rate) and drive continuous improvement.

 AI‑Enabled Data Quality (Key Expectation)

• Apply AI‑assisted capabilities to improve and scale data quality, including:
  • Intelligent profiling and anomaly detection
  • Automated rule suggestions and pattern discovery
  • Root‑cause insights and remediation prioritization
  • Duplicate detection and entity resolution, where applicable
• Ensure AI‑driven outcomes are transparent, governed, validated with business stakeholders, and aligned with compliance expectations.

Change Management, Communication & Culture

• Drive a data‑quality‑first mindset through communications, playbooks, training, and success stories.
• Provide executive‑ready reporting on progress, risks, and outcomes, with clear calls to action.
• Collaborate with Digital teams, analytics teams, and business functions to embed data quality standards into operating processes.

What You Need to Be Successful

• 8+ years of experience in Data Quality, Data Governance, or Master Data Management at an enterprise scale.
• Proven experience owning end‑to‑end data quality outcomes, including rule lifecycle management, monitoring cadence, remediation governance, and reporting.
• Hands‑on experience with Microsoft Purview for data cataloging, governance, stewardship enablement, and trust/visibility.
• Strong stakeholder management skills, working effectively across business teams, data stewards, and Digital teams within a global matrix organization.
• Ability to define data quality metrics, SLAs, dashboards, and scorecards, and to drive measurable, sustained improvements.
• Excellent communication, facilitation, and escalation skills, with the ability to influence without formal authority.
• Strong understanding of Critical Data Elements (CDEs) and attribute‑level data quality management.
• Exposure to SAP MDG and master data processes; familiarity with data quality tools such as SAP Data Services or SAP Information Steward is a plus.
• Experience implementing automated issue management and workflow integrations (e.g., ticketing or work management systems).
• Demonstrated experience applying AI/ML techniques to improve data quality, including profiling, anomaly detection, rule recommendation, and entity matching

Bonus Points If You Have

• Bachelor’s degree in information systems, Data Management, Business, Engineering, or equivalent experience.
• Experience with Microsoft Fabric (or equivalent modern data platforms) for data quality execution, monitoring, and analytics.
• Experience in automotive, manufacturing, or complex enterprise environments is desirable.
• Familiarity with data compliance and privacy regulations, such as GDPR.

What Makes You Eligible

• Willing to travel up to 5%, both domestically and internationally.
• Willing to work in an office or hybrid setup in Bangalore, India.

What We Offer

• Flexible work environment, allowing for full-time remote work globally for positions that can be performed outside a HARMAN or customer location
• Access to employee discounts on world-class Harman and Samsung products (JBL, HARMAN Kardon, AKG, etc.)
• Extensive training opportunities through our own HARMAN University
• Competitive wellness benefits
• Tuition reimbursement
• “Be Brilliant” employee recognition and rewards program
• An inclusive and diverse work environment that fosters and encourages professional and personal development

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