Job Purpose

Looking for a Data QA Engineer to lead data validation and regression testing across capital-markets data pipelines spanning legacy and 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.

The candidate will build reusable Python/SQL-based validation frameworks, support batch and cloud execution, validate post-trade data flows, 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.

Key Responsibilities

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.
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.
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

5–7 years of hands-on experience in Data QA, Data Engineering QA, or Python-based automation roles.
Atleast basic domain knowledge of Capital Markets with expertise in Fixed Income products and workflows
Strong 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.
Solid 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 Azure, AWS, ADF, containers, Kubernetes, or cloud-based data platforms.
Experience with relational and NoSQL databases such as SQL Server, MongoDB, Cassandra, DynamoDB, or similar platforms.
Exposure to Hadoop, Spark, Databricks, cloud data lakes, and Big Data validation.
Experience validating batch, streaming, real-time, and Kafka-based ingestion pipelines.
Capital markets domain exposure, especially post-trade, market data, reference data, trade data, or ION/blotter platform integrations.
Exposure to DevSecOps, infrastructure as code, and code quality tools such as Terraform, Bicep, Sonar, Mend, or ZAP.

Senior Associate-Front Office QA Engineer