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Tier-1 brand and metro location increase applicant density, but seniority and niche QA-data skillset moderate competition.
Data engineering and QA skills transfer broadly, though regulated finance context raises domain relevance.
Mandatory 15+ years and specific data testing, GCP, SQL/Python skills make shortlisting highly strict.
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Lead and technically guide data engineering testing and QA efforts to ensure reliable, scalable, and governed data pipelines and platforms.
Develop, execute, and automate SQL and script-based validations for data accuracy, completeness, and transformation logic in enterprise data workflows.
Collaborate with cross-functional teams for defect analysis, release support, and maintain test documentation per organizational standards.
15+ years of experience with strong hands-on expertise in data engineering testing, ETL validation, QA automation, or data warehouse environments.
Proficiency in SQL, Python, Unix/shell scripting, and experience with ETL tools like IICS or PowerCenter.
Experience with Google Cloud Platform (GCP) and cloud-based data engineering practices.
Work Experience Required: 15+ years; Notice Period: Not explicitly mentioned in the JD.
Proven track record in leading large-scale data validation and automation initiatives within Agile delivery frameworks.
Strong technical depth in automation script development, defect diagnosis, root cause analysis, and operationalizing test automation at scale.
Experience working in regulated, fast-paced enterprise environments with collaborative cross-functional teams.