Lead Data Quality Analyst

Purpose of Position

The Lead Data Quality Analyst is a strategic role responsible for driving enterprise-wide data quality initiatives, ensuring the accuracy, consistency, and reliability of data assets. This role leads the development and implementation of data quality frameworks, collaborates with cross-functional teams, and plays a key role in data governance and master data management (MDM) efforts. The Lead Analyst will work closely with data engineers, stewards, governance teams, and business stakeholders to establish standards, monitor data quality, and ensure alignment with organizational data strategy.

Tasks and Responsibilities

• Lead the design and implementation of enterprise data quality frameworks, rules, and validation processes
• Collaborate with engineering teams to validate complex ETL/ELT pipelines, Delta Lake tables, and data transformations
• Define and monitor data quality KPIs across structured and unstructured data sources
• Develop and maintain automated data quality monitoring systems, including alerts, thresholds, and dashboards
• Conduct advanced data profiling and root cause analysis to identify and resolve data quality issues
• Establish and enforce data quality standards, issue resolution workflows, and documentation practices
• Partner with Data Governance teams to align data quality efforts with governance policies and data stewardship models
• Support Master Data Management (MDM) initiatives by ensuring consistency and accuracy of critical data domains
• Collaborate with business units to define data ownership, stewardship roles, and data quality accountability
• Lead cross-functional data quality improvement initiatives and mentor junior analysts
• Ensure data integrity across the lakehouse architecture (Bronze, Silver, Gold layers)
• Contribute to the development of enterprise metadata management and data cataloging practices

Minimum Education

• Bachelor's degree from an accredited institution or equivalent industry experience

Minimum Experience

• 8+ years of experience in data quality, data governance, or data management roles
• 5+ years of hands-on experience with SQL and data validation techniques
• Proven experience leading data quality or governance initiatives in large-scale environments
• Experience with data warehousing, lakehouse platforms, and ETL/ELT processes
• Familiarity with MDM platforms and practices
• Proficient in BI tools such as Power BI for reporting and validation
• Experience working in Agile environments (e.g., Scrum, SAFe)

Knowledge, Skills, Abilities

• Expert-level SQL skills and experience with Databricks notebooks for data quality checks
• Strong understanding of data governance frameworks (e.g., DAMA-DMBOK) and best practices
• Experience with data quality and profiling tools (e.g., Informatica DQ, Great Expectations, Talend)
• Familiarity with MDM tools and concepts (e.g., Informatica MDM, SAP MDG, Reltio)
• Knowledge of enterprise platforms such as SAP ECC/S4HANA, Salesforce, or SuccessFactors
• Experience with metadata management and data cataloging tools (e.g., Informatica EDC, Alation, Collibra)
• Strong analytical, problem-solving, and leadership skills
• Excellent communication and stakeholder engagement abilities
• High attention to detail and a commitment to data accuracy and governance
• Ability to lead cross-functional teams and drive enterprise data quality strategy

Physical Demands and Work Environment

The physical demands and work environment described here are representative of those that must be met and/or encountered by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to use hands to finger, handle, or feel; reach with hands and arms; and communicate. The employee may be required to stand, walk, and sit. Specific vision abilities required by this job include the ability to view monitors, technical documents, and reference material. The noise level in the work environment is usually low to moderate.