About This Opportunity Responsibilities:

Lead the design, implementation, and continuous improvement of enterprise-wide data quality frameworks, standards, and best practices.
Define, monitor, and report on data quality KPIs, scorecards, and SLAs to measure data health across critical business domains.
Develop and maintain automated data quality checks, validation rules, reconciliation processes, and exception reporting frameworks.
Perform data profiling, anomaly detection, root-cause analysis, and impact assessments to identify and resolve data quality issues.
Write and optimize complex SQL queries to analyze, validate, and reconcile large and complex datasets across multiple source systems.
Develop Python-based automation solutions to streamline data validation, monitoring, reconciliation, and issue remediation processes.
Leverage AI/ML techniques and statistical methods to detect data anomalies, predict quality issues, and improve data observability.
Partner with Data Engineering teams to embed data quality controls within ETL/ELT pipelines and data transformation processes.
Collaborate with Data Governance, Business, and Technology teams to define critical data elements, business rules, and quality standards.
Ensure data integrity, consistency, completeness, accuracy, and timeliness across enterprise data platforms and reporting environments.
Support metadata management, data lineage, and data catalog initiatives to improve transparency and traceability of data assets.
Design and maintain dashboards, scorecards, and AI-enabled monitoring solutions that provide visibility into data quality performance, trends, and risks.
Lead investigations of data incidents, coordinate remediation efforts, and implement preventive controls to reduce recurring issues.
Mentor and guide analysts on data quality methodologies, Python programming, automation techniques, AI-driven analytics, and technical best practices.
Drive continuous improvement by identifying opportunities to leverage Generative AI, machine learning, and intelligent automation to enhance data quality processes and operational efficiency.

Educational & Required Qualifications:

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Statistics, Mathematics, Engineering, or a related quantitative field.
Minimum 7+ years of experience in Data Analytics, Data Quality, Data Governance, Business Intelligence, or related disciplines.
Proven experience leading data quality initiatives, projects, or teams within complex enterprise environments.
Advanced proficiency in SQL with hands-on experience querying, validating, reconciling, and analyzing large-scale datasets.
Strong programming skills in Python with experience developing automation solutions, data validation frameworks, APIs, and analytical workflows.
Experience applying AI/ML techniques, statistical analysis, or anomaly detection models to improve data quality, monitoring, and decision-making processes.
Experience working with modern cloud data platforms such as Databricks, Snowflake etc.
Solid understanding of data warehousing concepts, data modeling, ETL/ELT processes, data lifecycle management, and data governance principles.
Familiarity with Generative AI tools, AI-assisted analytics, automation frameworks, and modern data quality/observability platforms.
Excellent analytical, problem-solving, communication, stakeholder management, and cross-functional collaboration skills, with the ability to influence both business and technical teams.
Established data stewardship best practices and governance frameworks to improve data reliability.
Defined data ownership, stewardship responsibilities, and data quality standards across business units.
Automated data quality monitoring processes, reducing manual effort and improving data accuracy.
Partnered with data architects, data engineers, and business analysts to implement enterprise-wide governance solutions.

Preferred Technical Skills

SQL (Advanced)
Retail industry experience is preferred.
Python (Pandas, NumPy, PySpark, APIs, Automation Frameworks)
Data Quality & Observability Tools (Monte Carlo, Informatica DQ, etc.)
Power BI, Tableau, or similar BI platforms
Snowflake, Databricks, Azure, AWS, or GCP Data Services
Machine Learning & Anomaly Detection
Generative AI tools (Copilot, Windsurf, Codex, ChatGPT Enterprise, Azure OpenAI)
Git, CI/CD, and DevOps practices for analytics and automation solutions