Quality Engineering Lead - Data & Reporting Platforms

Citi; New York (NY); Hybrid; USD 37,000 - 74,000; Full time

Job summary

Citi is seeking a VP, Quality Engineering Lead to define and drive automated data and reporting testing for next‑gen AI powered ecosystems. You will lead QE initiatives across data virtualization, federation testing, data contracts, and AI interfaces.

You will manage a team, establish testing standards, and partner with engineering and product teams to deliver secure, high‑quality data and reports at scale.

Qualifications

• Minimum 10–12 years in software testing or data engineering.
• 5–8 years in automated data testing or data pipeline quality.
• 5–8 years in BI/report testing and data reconciliation.

Responsibilities

• Define end-to-end automated testing strategy for data virtualization and federation.
• Lead a team of QE engineers and promote modern QE practices.
• Collaborate with engineering and product teams to ensure quality and security.

Skills

Test strategy design

QE leadership

Python

Java

SQL

API testing

CI/CD

NLP testing

Education

Bachelor’s degree in CS/IS or equivalent

Experience in software testing or data engineering

Tools

Starburst

Trino

Presto

Denodo

Dremio

AWS Athena

Apache Drill

Tableau

Job description

Quality Engineering Lead – Data & Reporting Platforms

Job Req Id:

26981780

Location(s):

Pune, Maharashtra, India

Job Type:

Hybrid

Posted:

Aug. 14, 2026

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Job Overview

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 , 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.

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