Position Summary The Data Foundation Quality Engineering Lead drives the quality strategy across data products, pipelines, and services. This hands-on leader sets risk-based standards, advances our C#/ReqnRoll BDD automation framework, establishes release evidence, and mentors engineers. Partnering across Data Engineering, Architecture, Platform, Governance, and Operations, you will shift quality left to mitigate operational and data risks early.

Key Responsibilities
Strategy & Coverage: Execute a risk-based quality strategy across ingestion, transformation, storage, APIs, and analytics; align coverage to business-critical data elements (BDEs), data contracts, and SLOs. Automation & Architecture: Own and scale the C# / ReqnRoll BDD framework (including API, UI, and ExtentReports integration). Automated Validation: Build automated checks for source-to-target reconciliation, schema drift, API contracts, batch/event streams, and end-to-end consumer journeys. DevOps & Quality Gates: Define Azure DevOps CI/CD quality gates, regression runs, release evidence, production smoke tests, and recovery validation. Data Standards: Set best practices for database validation, deterministic test data generation, secure data handling, and governed data dictionaries.



Metrics & Observability: Track BDE coverage, reconciliation accuracy, escaped defects, test flakiness, and diagnostic lead time. Risk Triage & Leadership: Lead failure triage, run performance/security checks with specialist teams, communicate residual risk, and mentor engineers through code/assertion reviews. Qualifications

Experience:
Proven lead owning QE strategy for enterprise data platforms, ETL/ELT pipelines, distributed systems, or data-intensive services. Technical

Skills:
Solid C#, SQL, and BDD automation experience across databases, APIs, UI, message queues, files, and cloud data services. Frameworks & Tooling: Hands-on experience with ReqnRoll/SpecFlow, ExtentReports, Visual Studio, Git, and Azure DevOps (Pipelines, Repos, Test Plans). Data Quality Expertise: Deep knowledge of data reconciliation, contract/schema testing, metadata/lineage validation, and production failure diagnosis. Tools: Familiarity with BrowserStack, Requestly, DBeaver, and data dictionary management. Preferred Qualifications Experience with insurance data, canonical models, MDM, or regulated environments.

📌 Architect (Hyderabad)
🏢 Virtusa
📍 Hyderabad