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Tier-1 brand, metro location, and mid-level data role attract high candidate density.
Data quality skills transfer across industries, though regulated pharma experience adds moderate domain bias.
Explicit 5+ years plus mandatory SQL, Python, Databricks/Spark and cloud platform skills make filters strict.
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Design and implement scalable data quality solutions across multiple data architectures using SQL, Python, and Power Apps to ensure enterprise-grade data accuracy and reliability.
Lead data quality governance forums and root cause investigations, driving corrective actions and continuous process improvements to prevent recurrence of data issues.
Collaborate with business users and technical teams to document data quality rules, automate monitoring, and support metadata governance for AI-ready data products.
Bachelor’s degree in a quantitative field.
5+ years of experience in data quality management, data management systems, or data analytics roles.
Advanced SQL skills including CTEs, window functions, complex joins, and query optimization.
Strong Python programming skills and experience with cloud data platforms such as AWS, Azure, Snowflake, or Amazon Redshift.
Experienced in designing and automating data quality frameworks in large, global or regulated environments, preferably healthcare or pharmaceutical domains.
Skilled at cross-functional collaboration with business and technical stakeholders to resolve complex data quality issues and drive governance.
Proficient in using distributed data processing platforms like Databricks, Spark, or PySpark, with a focus on building reusable, scalable validation utilities.