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
Manual Testing + Strong Capital Market & Fixed income domain Experience + Python automation background