Sr. Lead QA SDET Data Engineering |

Technical Skills-:

Batch Processes Database/ETL processing Control-M jobs Database transformations and stored procedures. Good in SQL/ PL Develop custom test harnesses to support Batch and Sproc validations. Implement AI-assisted testing capabilities utilizing Devin.ai, GitHub Copilot, and comparable approved capabilities. Develop automated data validation scripts, regression suites, and custom data testing harnesses. Python

Job Description

We are seeking a Sr. Lead QA SDET to lead our Quality Engineering function for data and ETL platforms. This is the most senior technical-leadership role in the QE organization — responsible for defining automation and testing strategy, setting quality standards across teams, and providing hands-on technical direction for Python-based test automation across Snowflake, ETL/ELT pipelines, and Informatica workflows.

The ideal candidate is a hands-on automation expert who can also operate as a strategic quality leader, mentoring engineers and driving enterprise-wide QE and QA maturity.

Roles & Responsibilities

Key Responsibilities

Automation & Technical Leadership

Architect and own the enterprise test automation framework using Python, covering data validation, ETL/ELT testing, and reconciliation. Set coding standards, design patterns, and reusability guidelines for the automation framework. Lead automation of source-to-target validation, transformation logic checks, and aggregation testing across Snowflake and downstream systems. Provide technical oversight of Informatica-based ETL/ELT workflows, ensuring test coverage across mappings, workflows, and sessions. Drive CI/CD integration of automated test suites for continuous regression and data validation. Resolve the most complex data quality and automation issues across teams. Quality Assurance (QA) Leadership

Own the overall QA strategy: test planning, test design, functional/regression/integration/non-functional testing approach across data platforms. Define and enforce Quality Gates and release-readiness criteria (SIT → Regression → UAT → Production). Own defect management governance: triage, severity/priority standards, RCA on critical defects, defect trend and leakage analysis. Define QA metrics and dashboards (test pass rate, coverage, automation stability, defect aging, production defect leakage, data quality score) for executive and operational reporting. Establish and govern test data and test environment standards. Provide independent quality sign-off and release recommendations to leadership. Data & Domain Expertise

Act as the senior SME for data quality across business and technology stakeholders. Translate complex business rules into automated, measurable data quality checks. Identify enterprise-critical data flows and prioritize testing based on business risk. Leadership & Mentorship

Lead, mentor, and set technical direction for Lead and Senior QA SDETs. Facilitate quality governance forums; present risk, quality, and release readiness to leadership. Drive adoption of standardized QA/automation practices across teams without slowing delivery. Evaluate and responsibly introduce AI-assisted testing tools, with human review of AI-generated tests/scripts. Required Skills

Core (Mandatory):

Python – expert-level scripting and test automation framework design. Snowflake – expert hands-on experience in data validation and warehouse testing. ETL/ELT – expert understanding of pipelines, transformations, and validation. Informatica – strong hands-on and governance-level experience. Strong SQL and data warehouse concepts. Data profiling, reconciliation, and large-scale data validation frameworks. Quality Assurance (Mandatory):

Test strategy design (functional, regression, integration, non-functional). Defect management and RCA at governance level. Quality gates and release-readiness criteria definition. QA/QE metrics and dashboard design. Preferred:

CI/CD and DevOps integration. Cloud data platforms, data lakes/lakehouse. API testing. AI-assisted/GenAI testing experience.