Data Quality Analyst

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

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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., * 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, ? 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 data engineering and governance stakeholders

? 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

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? 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