Quality Engineering Lead – Data & Reporting Platforms

Citi; Pune District; Presencial; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

Citi is seeking a VP, Quality Engineering Lead in Pune to define and drive automated data and report testing across data virtualization, federation, and AI-powered interfaces. You will lead a team of QE professionals, establish standards, and partner with engineering and product teams to ensure secure, high-quality data and reports.

The role emphasizes end-to-end automation, governance, and integration into CI/CD pipelines, with a focus on scalable data platforms and large-scale migrations.

Formación

• Total testing experience: 10–12 years in software testing, quality engineering, or data engineering.
• Experience leading QE teams for data/BI platforms and large migrations.
• Strong automation, QA strategy, and data quality governance skills.

Responsabilidades

• Define and drive end-to-end automated data and report testing strategy.
• Lead a team of Data & Report Quality Engineers and mature QE practices.
• Validate data virtualization, federation, data contracts, and security testing.
• Test AI-enabled interfaces, NLP/SQL translation, and autonomous agents.
• Automate BI/report validation for large migrations and dashboards.
• Integrate testing into enterprise CI/CD pipelines and triage defects.

Conocimientos

Data virtualization

Federation testing

SQL

Python/Java

API testing

CI/CD

QA leadership

Educación

Bachelor’s degree in Computer Science / Information Systems

Herramientas

Starburst

Trino

Denodo

Descripción del empleo

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.

Time Type

Full time

Equal Opportunity and Diversity Statement

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.

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