Sr. Lead QA SDET Data Engineering |

Role Overview

Work Mode and Employment

• The role is hybrid and full-time in Bangalore.
• The source lists 8–12 years of experience, one opening, and an application deadline of October 3, 2026.

Automation and Technical Leadership

• Architect and own an enterprise Python automation framework for data validation, ETL/ELT testing, and reconciliation.
• Set coding standards, design patterns, and reusability guidelines for the automation framework.
• Lead source-to-target validation, transformation checks, and aggregation testing across Snowflake and downstream systems.
• Provide technical oversight of Informatica mappings, workflows, and sessions.
• Drive CI/CD integration of automated suites for continuous regression and data validation.
• Resolve complex data-quality and automation issues across teams.

Quality Assurance Leadership

• Own the QA strategy for functional, regression, integration, and non-functional testing across data platforms.
• Define quality gates and release-readiness criteria for SIT, regression, UAT, and production.
• Govern defect triage, severity and priority standards, critical-defect root-cause analysis, and defect trend reporting.
• Define QA metrics and dashboards covering pass rate, coverage, automation stability, defect aging, leakage, and data quality.
• Establish test-data and test-environment standards and provide release recommendations to leadership.

Data and Domain Expertise

• Act as the senior subject-matter expert for data quality across business and technology stakeholders.
• Translate business rules into automated, measurable data-quality checks.
• Identify critical data flows and prioritize testing based on business risk.

Leadership and Mentorship

• Lead, mentor, and set technical direction for Lead and Senior QA SDETs.
• Facilitate quality-governance forums and present risk, quality, and release readiness to leadership.
• Drive standardized QA and automation practices across teams.
• Evaluate and responsibly introduce AI-assisted testing tools with human review of generated tests and scripts.

Mandatory Skills

• Expert-level Python scripting and test automation framework design.
• Expert hands-on Snowflake experience in data validation and warehouse testing.
• Expert understanding of ETL/ELT pipelines, transformations, and validation.
• Strong hands-on and governance-level Informatica experience.
• Strong SQL and data warehouse concepts.
• Experience with data profiling, reconciliation, and large-scale data validation frameworks.
• Experience with test strategy, defect governance, quality gates, release readiness, and QA metrics.

Preferred Skills

• CI/CD and DevOps integration.
• Cloud data platforms, data lakes, or lakehouses.
• API testing.
• AI-assisted or generative-AI testing experience.

Hiring Process

• The source describes a four-stage process consisting of screening, a technical round, a culture-fit conversation, and an offer.