Senior Data Quality Engineering Lead

Role : Senior Data Quality Engineering Lead

Location: Onsite - Mountain View (3 days work from client office)

The priority order for this role

1. DQMS Operations
2. Governance & Metadata
3. Kafka / Streaming
4. Schema Governance
5. Leadership / Stakeholder Management
6. Observability / DQ SLAs
7. Java/Python Automation
8. AI / Agentic Enablement (high-value differentiator)

ABOUT THE ROLE

We are looking for a Senior Data Quality Engineering Lead to drive enterprise-scale data quality transformation across streaming operational and analytical data ecosystems.

This role goes beyond traditional data engineering - we need someone with hands-on experience building operating governing and continuously improving end-to-end Data Quality Management Systems (DQMS).

You will lead initiatives focused on improving the quality trustworthiness discoverability governance and operational integrity of critical enterprise data assets - identifying and correcting issues at the source-of-truth level enhancing governance workflows and evolving the systems that deliver reliable data to downstream consumers.

This is a highly cross-functional leadership role requiring deep expertise across data engineering governance metadata systems streaming platforms operational excellence and stakeholder management. You will also help shape AI/agentic accelerators to scale data quality operations and governance automation across the organization.


WHAT YOULL DO


Data Quality Engineering & Operations
Lead enterprise-wide initiatives to improve data quality standardization governance and operational reliability across streaming and analytical ecosystems
Own end-to-end operational management of DQMS - monitoring issue resolution remediation workflows governance enforcement and continuous improvement
Identify and address root causes of poor data quality at the source-of-truth level (not just downstream corrections)
Design and implement scalable processes to improve data accuracy completeness consistency lineage observability and compliance
Establish and operationalize data quality SLAs KPIs monitoring frameworks and escalation procedures

Data Governance & Metadata Management
Drive uplift of Kafka topics and data assets from 1 to 3 maturity using paved and non-paved workflows in MyData / DDE / DataMap ecosystems
Lead metadata enrichment schema governance ownership attestation compliance tagging and data stewardship initiatives
Partner with business compliance platform and engineering teams to define governance standards and operational best practices
Improve governance procedures workflows and tooling to ensure long-term sustainability and operational efficiency

Schema & Platform Engineering
Author review and govern IEDM schemas (YAML/Avro/JSON Schema) and associate them with production-grade streaming assets
Work closely with producer teams to improve data contracts event quality schema consistency and downstream usability
Coordinate operational activities across Kafka DataMap Studio DevPortal S3 metadata systems and promotion workflows
Troubleshoot and resolve complex promotion and compliance issues including multi-schema EventBus lineage and governance gaps

AI / Agentic Enablement
Define and scale AI-powered/agentic accelerators for automated DQ uplift metadata enrichment governance validation and operational remediation
Contribute reusable workflows operational patterns and automation capabilities to improve engineering productivity and governance scalability
Drive adoption of intelligent tooling and automation across the data quality lifecycle

Leadership & Collaboration
Act as a senior technical leader and trusted advisor across engineering governance analytics compliance and platform teams
Mentor engineers and help establish engineering standards governance playbooks and operational runbooks
Drive cross-functional execution and influence stakeholders across large enterprise environments


MUST-HAVE QUALIFICATIONS

8 years in Data Engineering Data Quality Engineering Data Governance or related enterprise data platforms
Strong operational experience managing enterprise-scale Data Quality Management Systems (DQMS)
Proven expertise identifying and correcting source-of-truth data issues and improving upstream data quality
Deep understanding of data governance metadata management lineage stewardship and compliance workflows
Hands-on experience with Kafka event-driven architectures and streaming data platforms
Strong schema management and governance experience - Avro JSON Schema YAML IEDM
Experience with enterprise metadata/catalog platforms - DataHub Collibra Alation MyData or DataMap
Strong Java and/or Python skills for working with producer systems and automation workflows
Experience establishing operational metrics DQ SLAs observability monitoring and remediation frameworks
Excellent stakeholder management and cross-functional communication skills


NICE TO HAVE

Experience with CDC pipelines lakehouse architectures and modern data platforms
Exposure to regulatory/compliance frameworks - SOX CCPA IRS 7216 GDPR PII governance
Experience building or leveraging AI/LLM/agentic frameworks for engineering productivity or governance automation
Familiarity with data observability platforms and automated DQ tooling
Experience in fintech payments tax or highly regulated enterprise environments
Experience leading distributed/global engineering teams


WHAT SUCCESS LOOKS LIKE IN 90 DAYS

Owns and drives end-to-end uplift of critical data assets and Kafka topics to 3 maturity
Establishes operational governance and DQ monitoring mechanisms for assigned domains
Resolves complex upstream data quality issues impacting downstream consumers
Delivers measurable improvements in metadata completeness governance compliance and operational efficiency
Contributes reusable automation workflows or AI-powered accelerators for DQ operations
Creates durable operational runbooks and governance playbooks adopted by multiple teams
Becomes a trusted technical and operational leader across