Senior Associate- Manual+Automation QA

Job Purpose

Seeking a Manual & Automation QA Engineer with strong Capital Markets and Fixed Income expertise to drive functional, integration, end-to-end, and data quality testing across trading, post-trade, investment management, and data platforms. The role is responsible for ensuring application quality, data accuracy, business rule compliance, and regulatory adherence through manual testing, source-to-target validation, reconciliation, and automation-led regression testing. The ideal candidate will leverage Python/SQL-based automation frameworks, validate complex fixed-income and post-trade workflows, and collaborate closely with business and engineering teams to deliver high-quality, reliable solutions across legacy and modern technology environments.

Key Responsibilities

• Perform functional, system, integration, and end-to-end testing across fixed income applications
• Validate complete trade lifecycle events across asset classes including trade capture, order management, execution, confirmation, clearing, settlement, corporate actions, and reporting.
• Perform testing of derivative products and workflows including Options, Swaps, Futures, and Forwards.
• Validate business workflows covering bonds, treasuries, corporate debt, and other fixed income instruments
• Verify trade booking, valuations, cashflows, margin processing, lifecycle events, and settlement workflows across asset classes.
• Create, execute, and maintain comprehensive test cases, test scenarios, and regression test suites
• Perform requirement analysis and ensure test coverage aligned with business and regulatory requirements
• Collaborate with business analysts, traders, product owners, and development teams to validate business functionality
• Support testing across legacy platforms and modern cloud-based applications
• 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.
  
  

Key competencies

• Strong experience in Manual Testing methodologies and QA best practices
• Solid understanding of Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC)
• Strong experience in test case design, defect management, and regression testing
• Deep domain knowledge of Capital Markets with expertise in Fixed Income products and workflows
• Experience validating trade lifecycle processes including order management, execution, allocations, settlements, and reporting
• Proficiency in leveraging AI-assisted tools (e.g., Claude, GitHub Copilot) to accelerate test case generation, requirement analysis, defect investigation, test documentation, and test data preparation
• Hands-on experience in Data QA, Data Engineering QA, or Python-based automation roles.
• Good Python skills for data validation, automation framework development, regression testing, and pipeline verification.
• Working knowledge of PySpark for validating large-scale data pipelines and distributed datasets.
• Good SQL skills for source-to-target validation, reconciliation, complex queries, stored procedures, schema checks, and defect analysis.
• Experience in data QA, ETL/ELT testing, data pipeline testing, and regression testing across legacy and modern platforms.
• Proven experience building automation-first regression frameworks, reusable validation scripts, test cases, and data verification utilities.
• Strong understanding of data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, referential integrity, and business-rule conformance.
• Experience validating metadata, schema structures, data types, constraints, lineage, transformation logic, and downstream outputs.
• Exposure to CI/CD tools such as Azure DevOps, Jenkins, or GitLab, with the ability to integrate automated tests and quality gates into deployment pipelines.
• Familiarity with batch orchestration tools such as AutoSys, Control-M, or Airflow.
• Good analytical skills to understand post-trade, financial, market data, or reference data and translate them into repeatable validation scenarios.
• Experience with JIRA, XRay, agile delivery practices, defect management, and QA process governance.
• Proficiency in leveraging AI-assisted tools such as Claude or GitHub Copilot to accelerate test case generation, SQL development, data validation, defect analysis, documentation, and test data creation.
• Strong communication, problem-solving, attention to detail, and cross-functional collaboration skills.
  
  

Nice to Have

• Exposure to test management and defect tracking tools such as JIRA, ALM, or similar tools
• Experience with market data, reference data, pricing, and security master validation
• Understanding of fixed income analytics including yields, durations, coupon calculations, and bond pricing
  
  

concepts

• Experience validating interfaces between front-office, middle-office, and back-office systems
• Knowledge of regulatory reporting and compliance processes relevant to capital markets environments
• Exposure to SQL for data validation and support of backend testing activities