Manager, Data Quality Engineering

As a Manager Data Quality Engineering you will lead the organizations data quality quality engineering and data analyst practice. This is a senior technical leadership role accountable for ensuring the reliability trustworthiness and operational excellence of data pipelines and data products across analytics AI and operational platforms.

You will partner closely with Data Engineering Platform Analytics Product and Business teams to embed quality-by-design into data pipelines implement automated testing and observability and run production data operations. The role combines proactive quality engineering with hands-on operational leadershipensuring data issues are detected early resolved quickly and prevented from recurring at scale.

General Responsibilities:

Leadership Team Development & Practice Building 

• Own the quality engineering practice end-to-end  vision strategy operating model and roadmap. You are responsible for maturing QE from a support function into a core engineering discipline. 
• Partner with Data Engineering to ensure pipelines are resilient observable and aligned to business requirements.
• Build develop and retain a high-performing team of quality engineers and analysts (onshore offshore). Set clear expectations provide regular feedback and create growth paths for your team members. 
• Define and govern QE standards processes and KPIs  including automation coverage cycle time defect leakage test effectiveness and data validation coverage across all Lines of Business. 
• Establish a culture of engineering rigor and accountability  where quality is everyones responsibility not a gate at the end of the pipeline. 
• Create a knowledge repository that replaces tribal knowledge enterprise test strategy reusable patterns and documented standards that scale beyond any individual. 
• Evaluate adopt and govern data quality and observability tools (build vs. buy) e.g. Great Expectations Soda Monte Carlo QuerySurge or custom Databricks-native frameworks. 
• Build quality into data pipelines through preventive design automated testing and CI/CD quality gates.
• Design and maintain automated checks for freshness completeness accuracy validity volume and schema drift.
• Establish enterprise data quality frameworks scorecards SLAs/SLOs and standards for critical datasets.

Hands-On Technical Leadership

• Stay close to the work by participating in design reviews architecture discussions and technical decision-making ensuring quality is designed in not tested in. 
• Guide the team in building automated data validation frameworks (Python PySpark SQL) covering data comparison regression BI report validation and pipeline smoke tests. 
• Drive the embedding of quality gates into CI/CD pipelines  freshness completeness accuracy validity volume schema drift and business rule conformance checks before production deployment. 
• Architect and oversee data quality observability  dashboards alerting SLA-aligned thresholds and escalation paths for engineers product owners and leadership. 
• Lead incident response for critical data quality issues  guide triage RCA post-mortems and corrective actions. Reduce MTTR through automation and operational playbooks. 
• Selectively contribute hands-on to high-impact POCs automation frameworks and complex debugging setting the technical standard through your own work when it matters most. 

Cross-Functional Partnership

• Partner with Data Engineering to ensure pipelines are resilient observable and aligned to business requirements. 
• Collaborate with Analytics Product and Business stakeholders to align quality metrics to business outcomes. 
• Support AI/ML initiatives by ensuring reliable high-quality training and inference data. 
• Work with platform teams (Databricks Azure CI/CD tooling) to embed quality signals natively into orchestration and release workflows. 

Qualifications :

Must-have Skills & Experience

• 8 years in data engineering analytics engineering data quality or data operations with 2 years in a lead senior lead or management role. 
• Demonstrated ability to build mentor and develop engineering talent  you know how to grow people set expectations and create accountability. 
• Strong technical judgment across data quality engineering QA and production data operations you can evaluate designs guide architecture decisions and hold your team to high technical standards. 
• Proficiency in SQL and working knowledge of Python/PySpark  enough to review code guide automation design and contribute hands-on when needed. You dont need to be the best coder on the team but you need to be technically credible. 
• Experience with modern cloud data platforms (Databricks Delta Lake Azure Data Lake cloud data warehouses/lakehouses). 
• Experience embedding quality into CI/CD workflows  quality gates automated regression and release automation for data pipelines. 
• Experience leading or significantly contributing to incident response RCA and reliability improvement in production environments. 
• Ability to translate technical issues into clear business impact for executive and cross-functional audiences. 

Nice to Have

• Experience with data quality and observability tools (Monte Carlo Great Expectations Soda QuerySurge or custom frameworks). 
• Familiarity with orchestration and workflow tools (Control-M Azure Data Factory Databricks Workflows). 
• Experience supporting regulated or high-scale enterprise environments with production SLA governance. 
• Knowledge of data governance metadata management Unity Catalog and data cataloging. 
• Experience with streaming data platforms (Kafka/Confluent) and schema management. 
• Exposure to dimensional modeling data warehousing and query performance tuning. 
• Experience with BI tools semantic layers or managing data product SLAs. 

Education & Experience

BS/MS in Computer Science Information Systems Data/Analytics or equivalent practical experience.

Additional Information :

Benefits:
    Paid Holidays and Vacation 
    Medical Dental & Vision benefits that start on the first day of employment
    No-cost mental health support for employee and dependents
    Childcare tuition discounts
    No-cost fitness nutrition and wellness programs 
    Fertility benefits
    Adoption assistance
    401k matching contributions 
    15% off the purchase price of stock 
    Company bonus 
 

All your information will be kept confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Full-time