Software Development Engineer in Test (SDET)

We are looking for a Software Development Engineer in Test (SDET) to design, build, and maintain automation-first test frameworks for data validation and regression testing across capital markets data pipelines spanning legacy and modern platforms. This is a hands-on engineering role focused on building scalable, reusable test automation solutions (Python/PySpark/SQL) that validate data accuracy, completeness, consistency, integrity, and business-rule conformance across post-trade and enterprise data ecosystems.

Please share an updated profile to

[email protected]

(+91)74839 25904

Mode of Work:Hybrid

Job Location:Gurugram,Pune,Bangalore

Key Responsibilities

Test Automation & Framework Development.

• Design, build, and maintain automation-first regression testing frameworks using Python, PySpark, and SQL.

• Develop reusable validation utilities, libraries, APIs, and CLI tools to support data validation, orchestration, and reporting workflows.

• Architect scalable test frameworks supporting batch and cloud-based execution models.

• Build field-level, dataset-level, transformation-level, and aggregate-level automated data checks across source, staging, warehouse, data lake, and downstream systems.

Data Validation Engineering

• Perform source-to-target data validation across legacy and modern systems, including SQL Server and cloud-based environments.

• Automate validation of data accuracy, completeness, consistency, timeliness, duplication handling, referential integrity, schema structures, metadata, and lineage.

• Validate ETL/ELT pipelines, post-trade data flows, stored procedures, batch jobs, and cloud-based execution workflows.

- 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.

CI/CD & Pipeline Integration

• 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 dev, UAT, and production environments.

• Work with engineering teams to embed data quality controls into pipelines, releases, and operational workflows.

Quality Engineering & Defect Analysis

• Design reusable test cases, validation scenarios, reconciliation checks, and regression packs for repeatable data-quality assurance.

• 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.

Collaboration

• Partner with engineering, business, and product teams to define requirements, resolve issues, and ensure test coverage.

• Contribute to test strategy, quality standards, and best practices as part of an Agile delivery team.

• Communicate test status, risks, and quality metrics clearly to technical stakeholders.

Key Competencies / Requirements

• 8–10 years of hands-on experience as an SDET, Test Automation Engineer, or Data Engineer in QA, with a strong software engineering background.

• Strong Python skills for building automation frameworks, validation utilities, and regression test suites (not just running scripts, but designing maintainable, reusable code).

• Working knowledge of PySpark for validating large-scale, distributed data pipelines.

• Solid SQL skills for source-to-target validation, reconciliation, complex queries, stored procedures, and schema checks.

- Practical experience with ETL/ELT testing, data pipeline testing, and regression automation across legacy and modern platforms.

• Strong understanding of data quality dimensions: 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.

• Hands-on experience with CI/CD tools such as Azure DevOps, Jenkins, or GitLab, and integrating automated tests/quality gat…