Quality Engineering Lead – Data & Reporting Platforms

Citigroup Inc.; Pune District; Hybrid; INR 6,000,000 - 9,000,000; Full time

Job summary

Citi is seeking a VP, Quality Engineering Lead to define and drive automated data and reporting test strategies across data virtualization, federation, and AI-enabled interfaces. You will lead a team of QE professionals, set standards, and ensure secure, high-quality data delivery across BI platforms and dashboards.

You will oversee automated data contract testing, schema validation, and integration of QA pipelines into CI/CD, partnering with engineering and product teams to scale testing across

Qualifications

• Minimum 10–12 years in software testing or quality engineering, with data/analytics emphasis.
• 5–8 years in automated data testing, ETL testing, or data pipeline QA.
• 5–8 years verifying BI/reporting and data reconciliation.

Responsibilities

• Define and execute end-to-end automated testing strategy for data virtualization, federation, BI reporting, and AI-enabled interfaces.
• Lead and mentor a team of Data & Report Quality Engineers; establish QE standards and governance.
• Validate data contracts, schemas, access controls, and security testing across analytics pipelines.

Skills

Data virtualization

Federation testing

BI & reporting validation

SQL proficiency

Python or Java

API testing

CI/CD integration

QA leadership

Education

Bachelor’s degree in CS/IS or equivalent

Tools

Postman

RestAssured

Jenkins

GitLab CI

Job description

## Quality Engineering Lead – Data & Reporting PlatformsApply: Hybrid: Pune Maharashtra India: Full time: Posted Today: End Date: October 27, 2026 (13 days left to apply): 26981780# Role OverviewWe 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.* *View Citi’s EEO Policy Statement and the Know Your Rights poster.*