Quality Assurance Lead

Job purpose: -

Looking for a Test Lead / Data QA Engineer to drive and oversee data validation, manual testing, and regression testing activities across capital markets data pipelines spanning both legacy and modern platforms. The role is responsible for ensuring data accuracy, completeness, consistency, integrity, and adherence to business rules through source-to-target validation, schema verification, data reconciliation, lineage analysis, and automation-led regression testing.

The successful candidate will lead test delivery, manage and coordinate offshore QA resources, and engage closely with business and technology stakeholders to maintain high-quality data across post-trade and enterprise data ecosystems. Key responsibilities include building reusable Python- and SQL-based validation frameworks, supporting batch and cloud-based execution models, identifying data anomalies, validating end-to-end data flows, and embedding robust data quality controls throughout the development lifecycle.

The ideal candidate combines strong data QA and testing expertise, hands-on automation skills, capital markets domain knowledge, and a proactive ownership mindset to ensure data quality, consistency, and reliability across environments.

Key responsibilities:

• Lead end-to-end test planning, execution, and delivery for data validation and regression testing initiatives, ensuring quality, schedule, and stakeholder expectations are met.
• Manage and mentor offshore/onshore QA teams, including resource planning, task allocation, deliverable reviews, and adherence to testing best practices.
• Drive test governance, including test strategy, defect management, risk tracking, quality metrics, status reporting, and release readiness assessments.
• Collaborate with business, engineering, and product teams to define requirements, resolve issues, ensure test coverage, and embed quality controls across the delivery lifecycle.
• Perform source-to-target data validation across legacy and modern systems, including SQL Server and cloud-based data environments.
• Validate data accuracy, completeness, consistency, timeliness, duplication handling, referential integrity, schema structures, metadata, lineage, and business-rule conformance.
• Build and maintain automation-first regression testing frameworks using Python, PySpark, SQL, and reusable validation utilities.
• Execute field-level, dataset-level, transformation-level, and aggregate-level data checks across source, staging, warehouse, data lake, and downstream systems.
• Validate ETL/ELT pipelines, post-trade data flows, stored procedures, batch jobs, and cloud-based execution workflows.
• Design reusable test cases, validation scenarios, reconciliation checks, automated scripts, and regression packs for repeatable data-quality assurance.
• Write, optimize, and troubleshoot complex SQL queries and stored procedures across SQL Server, Oracle, PostgreSQL, or similar platforms.
• Translate business rules, stored procedure logic, and post-trade process flows into automated validation scripts and assertions.
• Build Python-based utilities, APIs, CLI tools, and reusable frameworks to support data validation, orchestration, API integrations, and reporting workflows.
• Integrate regression tests, data validation checks, SQL deployables, and quality gates into CI/CD pipelines using Azure DevOps, Jenkins, GitLab, or equivalent tools.
• Automate execution of AutoSys jobs, batch processes, stored procedures, and validation scripts across development, UAT, and production environments.
• Work with engineering teams to embed data quality controls into pipelines, releases, and operational workflows.
• Identify data anomalies, schema mismatches, duplicate records, null-handling issues, transformation failures, reconciliation breaks, and integrity violations.
• Log defects with clear evidence, impact analysis, root-cause observations, and business context.
• Support capital markets and post-trade validation across allocations, clearing, settlement, confirmations, reconciliations, market data, reference data, and downstream reporting.