QA Data Engineer

Acuity Analytics; Pune District; Hybrid; INR 1,500,000 - 2,200,000; Full time

Job summary

Acuity Analytics is seeking a data validation engineer in Pune to ensure data quality across legacy and cloud environments. You will implement automated regression checks, validate ETL/ELT pipelines, and verify stored procedures in a multi-database setup.

Responsibilities include designing reusable test frameworks, executing comprehensive data checks, and integrating validation scripts with CI/CD workflows.

Qualifications

• 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.

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.

Skills

Python scripting

Data validation

Automation testing

CI/CD

SQL

Tools

PySpark

SQL Server

Oracle

PostgreSQL

Azure DevOps

Jenkins

GitLab

Job description

Role & responsibilities

Python scripting for data validation, Proficiency in leveraging AI-assisted tools such as Claude or GitHub Copilot, PySpark data pipeline validation, SQL querying and database testing, source-to-target data validation, ETL/ELT testing, automation-first regression framework development, data quality validation, stored procedure testing, post-trade capital markets data validation

Preferred candidate profile

• 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.