Senior Associate-Front Office QA Engineer

<p><span style="font-size:14px;"><b><u>Job purpose </u></b></span></p><p><span style="font-size:14px;">Looking for a Data QA Engineer to lead data validation and regression testing across capital-markets data pipelines spanning legacy and </span>modern platforms. The role will focus on ensuring data accuracy, completeness, consistency, integrity, and adherence to business rules through source-to-target validation, schema checks, reconciliation, lineage review, and automation-led regression testing. </p><p><span style="font-size:14px;">The candidate will build reusable Python/SQL-based validation frameworks, support batch and cloud execution, validate post-trade data flows, </span>identify data anomalies, and partner with engineering and business teams to embed strong data-quality controls. The ideal candidate combines data QA expertise, capital-markets domain understanding, automation-first thinking, and strong ownership of data quality across environments. </p><p><span style="font-size:14px;"><b><u>Key responsibilities </u></b></span></p><p><span style="font-size:14px;">• Perform source-to-target data validation across legacy and modern systems, including SQL Server and cloud-based </span>data environments. </p><p><span style="font-size:14px;">• Validate data accuracy, completeness, consistency, timeliness, duplication handling, referential integrity, schema </span>structures, metadata, lineage, and business-rule conformance. </p><p><span style="font-size:14px;">• Build and maintain automation-first regression testing frameworks using Python, PySpark, SQL, and reusable </span>validation utilities. </p><p><span style="font-size:14px;">• Execute field-level, dataset-level, transformation-level, and aggregate-level data checks across source, staging, </span>warehouse, data lake, and downstream systems. </p><p><span style="font-size:14px;">• Validate ETL/ELT pipelines, post-trade data flows, stored procedures, batch jobs, and cloud-based execution </span>workflows. </p><p><span style="font-size:14px;">• Perform testing of derivative products and workflows including Options, Swaps, Futures, and Forwards. </span></p><p><span style="font-size:14px;">• Validate business workflows covering bonds, treasuries, corporate debt, and other fixed income instruments </span></p><p><span style="font-size:14px;">• Verify trade booking, valuations, cashflows, margin processing, lifecycle events, and settlement workflows across </span>asset classes. </p><p><span style="font-size:14px;">• Design reusable test cases, validation scenarios, reconciliation checks, automated scripts, and regression packs for </span>repeatable data-quality assurance. </p><p><span style="font-size:14px;">• Write, optimize, and troubleshoot complex SQL queries and stored procedures across SQL Server, Oracle, PostgreSQL, </span>or similar platforms. </p><p><span style="font-size:14px;">• Translate business rules, stored procedure logic, and post-trade process flows into automated validation scripts and </span>assertions. </p><p><span style="font-size:14px;">• Build Python-based utilities, APIs, CLI tools, and reusable frameworks to support data validation, orchestration, API </span>integrations, and reporting workflows. </p><p><span style="font-size:14px;">• Integrate regression tests, data validation checks, SQL deployables, and quality gates into CI/CD pipelines using Azure </span>DevOps, Jenkins, GitLab, or equivalent tools. </p><p><span style="font-size:14px;">• Automate execution of AutoSys jobs, batch processes, stored procedures, and validation scripts across development, </span>UAT, and production environments. </p><p><span style="font-size:14px;">• Work with engineering teams to embed data quality controls into pipelines, releases, and operational workflows. </span></p><p><span style="font-size:14px;">• Identify data anomalies, schema mismatches, duplicate records, null-handling issues, transformation failures, </span>reconciliation breaks, and integrity violations. </p><p><span style="font-size:14px;">• Log defects with clear evidence, impact analysis, root-cause observations, and business context. </span></p><p><span style="font-size:14px;">• Support capital markets and post-trade validation across allocations, clearing, settlement, confirmations, </span>reconciliations, market data, reference data, and downstream reporting. </p><p><span style="font-size:14px;"><b><u>Key competencies </u></b></span></p><p><span style="font-size:14px;">• 5–7 years of hands-on experience in Data QA, Data Engineering QA, or Python-based automation roles. </span></p><p><span style="font-size:14px;">• Atleast basic domain knowledge of Capital Markets with expertise in Fixed Income products and workflows </span></p><p><span style="font-size:14px;">• Strong Python skills for data validation, automation framework development, regression testing, and pipeline verification. </span></p><p><span style="font-size:14px;">• Working knowledge of PySpark for validating large-scale data pipelines and distributed datasets. </span></p><p><span style="font-size:14px;">• Solid SQL skills for source-to-target validation, reconciliation, complex queries, stored procedures, schema checks, and defect </span>analysis. </p><p><span style="font-size:14px;">• Experience in data QA, ETL/ELT testing, data pipeline testing, and regression testing across legacy and modern platforms. </span></p><p><span style="font-size:14px;">• Proven experience building automation-first regression frameworks, reusable validation scripts, test cases, and data </span>verification utilities. </p><p><span style="font-size:14px;">• Strong understanding of data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, </span>referential integrity, and business-rule conformance. </p><p><span style="font-size:14px;">• Experience validating metadata, schema structures, data types, constraints, lineage, transformation logic, and downstream </span>outputs. </p><p><span style="font-size:14px;">• Exposure to CI/CD tools such as Azure DevOps, Jenkins, or GitLab, with the ability to integrate automated tests and quality </span>gates into deployment pipelines. </p><p><span style="font-size:14px;">• Familiarity with batch orchestration tools such as AutoSys, Control-M, or Airflow. </span></p><p><span style="font-size:14px;">• Good analytical skills to understand post-trade, financial, market data, or reference data and translate them into repeatable </span>validation scenarios. </p><p><span style="font-size:14px;">• Experience with JIRA, XRay, agile delivery practices, defect management, and QA process governance. </span></p><p><span style="font-size:14px;">• Proficiency in leveraging AI-assisted tools such as Claude or GitHub Copilot to accelerate test case generation, SQL </span>development, data validation, defect analysis, documentation, and test data creation. </p><p><span style="font-size:14px;">• Strong communication, problem-solving, attention to detail, and cross-functional collaboration skills. </span></p><p><span style="font-size:14px;"><b>Nice to Have </b></span></p><p><span style="font-size:14px;">• Exposure to Azure, AWS, ADF, containers, Kubernetes, or cloud-based data platforms. </span></p><p><span style="font-size:14px;">• Experience with relational and NoSQL databases such as SQL Server, MongoDB, Cassandra, DynamoDB, or similar platforms. </span></p><p><span style="font-size:14px;">• Exposure to Hadoop, Spark, Databricks, cloud data lakes, and Big Data validation. </span></p><p><span style="font-size:14px;">• Experience validating batch, streaming, real-time, and Kafka-based ingestion pipelines. </span></p><p><span style="font-size:14px;">• Capital markets domain exposure, especially post-trade, market data, reference data, trade data, or ION/blotter platform </span>integrations. </p><p><span style="font-size:14px;">• Exposure to DevSecOps, infrastructure as code, and code quality tools such as Terraform, Bicep, Sonar, Mend, or ZAP. </span></p>