**Role Overview** We are seeking a highly skilled and experienced **VP, Quality Engineering Lead** to define, build, and drive our automated data and report testing strategy. In this role, you will lead the Quality Engineering (QE) initiatives for our next-generation, AI-powered data and reporting ecosystem. As a hands-on leader, you will design robust automated test suites to validate complex data architectures-specifically focusing on data virtualization, massive data federation, data contract testing, and the verification of emerging natural language/conversational AI query interfaces. You will manage a talented team of quality engineers, establish testing standards, and collaborate closely with engineering, and product teams to ensure high-quality, secure, and performant data and report delivery. **Key Responsibilities** **1. Test Strategy & Quality Leadership** + **Data & Reporting Test Strategy:** Architect and execute a comprehensive, end-to-end automated testing strategy covering data virtualization, federated queries, BI/reporting, and AI-enabled analytical interfaces. + **Team Leadership:** Lead, mentor, and functionally manage a specialized team of Data & Report Quality Engineers, fostering a culture of modern Quality Engineering (QE) and continuous improvement. + **Governance & Compliance:** Define operating standards, automated quality gates, and data verification protocols across the analytics and reporting delivery lifecycle. + **Stakeholder Management:** Own the reporting of quality metrics, pipeline coverage, and test automation maturity to senior global technology and engineering leaders. **2. Data Virtualization & Federation Testing** + **Federated Query & Virtualization Validation:** Develop automated testing frameworks to validate query execution, latency, and data integrity across massive federated query engines and data virtualization platforms (e.g., **Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill** ) connecting dozens of heterogeneous catalogs without physical data movement. + **Data Contract & Schema Validation:** Implement automated schema validation and data contract testing to ensure curated, virtualized data products strictly adhere to published business definitions and system requirements. + **Access Control & Security Testing:** Design data-driven security tests to verify that centralized data access governance (e.g., Apache Ranger, role/attribute-based access controls) and data masking are flawlessly applied. **3. AI & Conversational Intelligence Testing** + **Natural Language Query Testing:** Establish frameworks to test conversational AI interfaces that allow users to query data using natural language. Validate natural language processing (NLP) models, intent recognition, NLP-to-SQL translation logic, and the accuracy of the underlying datasets returned. + **Autonomous Agent Verification:** Design testing patterns for non-deterministic AI agents (e.g., automated alerting systems and contextual research assistants), validating logical outputs, threshold actions, and boundary limits.



**4. Big Data & Reporting Platform Testing** + **Report & Dashboard Verification:** Devise automated strategies to test visual correctness, performance, and backend data reconciliation for BI platforms (e.g., Tableau, custom web-based dashboards) during large-scale migration phases of legacy systems (comprising hundreds of reports). + **Data Lakehouse & Pipeline Testing:** Lead automation efforts validating complex data pipelines across hybrid databases (Oracle, SQL Server) and modern analytical lakehouses. + **Data Reconciliation:** Design and automate source-to-target data reconciliation, schema drift detection, and data lineage validation to ensure reports match underlying source systems perfectly. **5. CI/CD & Test Automation Engineering** + **Continuous Quality Pipelines:** Seamlessly integrate data and report automation suites into enterprise CI/CD pipelines (Jenkins, Tekton, GitLab, etc.) to trigger continuous verification with each deployment code path. + **Triage & Defect Management:** Champion structured defect triage, prioritizations, and root cause analysis across complex, multi-tiered data and reporting infrastructure environments. **Technology Skills** **Required Technical Skillsets** + **Data Virtualization & Federation:** Hands-on experience with enterprise data virtualization or query federation platforms, such as **Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill** . + **BI & Reporting Platforms:** Deep expertise in testing BI and reporting platforms (e.g., Tableau, custom web-based dashboards, Aspose, or similar reporting engines). + **Database Querying & Testing:** Advanced SQL expertise with hands-on experience testing relational databases (Oracle, SQL Server) and NoSQL databases. + **Programming Languages:** Proficiency in **Python** or **Java** to build, maintain, and scale custom test automation frameworks. + **API Testing:** Strong experience with API testing (REST/SOAP) and data contract validation using tools like Postman, RestAssured, or custom scripts. + **CI/CD Integration:** Experience integrating automated data test suites into enterprise CI/CD pipelines (e.g., Jenkins, Tekton, GitLab CI) to enable continuous testing. + **Test Methodologies:** Deep understanding of Agile/Scrum methodologies, functional, integration, regression, and parallel-run testing for large-scale migrations. **Preferred / Nice-to-Have Skillsets** + **Modern Lakehouse & Warehouse Platforms:** Familiarity with cloud-native data platforms, such as **Databricks** or **Snowflake** (experience with Google BigQuery is also valued).



+ **Distributed Data Processing Engines:** Familiarity with distributed compute engines, specifically **Apache Spark** (PySpark, Spark SQL) or **Apache Flink** for large-scale data processing. + **Data Quality Automation:** Experience implementing automated data quality frameworks using industry-standard tools such as **Great Expectations** , **dbt test** , **Soda / SodaCL** , or **Deequ / PyDeequ** . + **AI/ML & NLP Testing:** Experience testing LLM-backed applications, validating Natural Language-to-SQL engines (e.g., conversational query interfaces), prompt validation, and autonomous agent testing. **Leadership & Methodology** + **Agile QE Leadership:** Strong experience running QA cycles within Scrum/Kanban frameworks, managing sprint closures, and collaborating with cross-functional Dev/Product leads. + **Test Strategy Design:** Proven track record of designing multi-layered testing strategies (unit, integration, regression, system, and regression parallel runs for migrations). **Experience & Qualifications** + **Total Testing Experience:** Minimum 10-12 years of relevant experience in software testing, quality engineering, or data engineering. + **Data Automation Experience:** Minimum 5-8 years of hands-on experience in automated data testing, ETL testing, or data pipeline quality engineering. + **BI & Analytics Verification:** Minimum 5-8 years of experience in report/BI testing, data reconciliation, and source-to-target data validation. + **Education:** Bachelor's degree in Computer Science, Information Systems, or equivalent engineering field. ------------------------------------------------------ **Job Family Group:** Technology ------------------------------------------------------ **Job Family:** Applications Development ------------------------------------------------------ **Time Type:** Full time ------------------------------------------------------ **Most Relevant Skills** Please see the requirements listed above. ------------------------------------------------------ **Other Relevant Skills** For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------ _Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law._ _If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review_ _Accessibility at Citi (https://www.citigroup.com/citi/accessibility/application-accessibility.htm)_ _._ _View Citi's_ _EEO Policy Statement (https://www.citigroup.com/global/eeo-aa-policy)_ _and the_ _Know Your Rights (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf)_ _poster._ Citi is an equal prospect and affirmative action employer. Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity.

📌 Quality Engineering Lead - Data & Reporting Platforms (Pune)
🏢 Citigroup
📍 Pune