Job Description
About Kestra:
Kestra is a U.S.-based wealth management platform built to support independent financial professionals and the clients they serve. Together, we provide technology, investment platforms, operational and business services, compliance and reporting, and specialized expertise that help advisors focus on what matters most - building trusted relationships and helping clients achieve meaningful financial goals. As one of the fastest growing organizations in the wealth management space, Kestra is building its India Innovation Center in Bangalore to help shape how the organization operates and scales. Disclaimer: Associates at the Kestra India are employed by ANSR, which owns and operates the center. They provide technology and business services for Kestra.
Key Responsibilities: Quality Engineering & Test Strategy: Define and implement
end-to-end quality engineering strategies
for data platforms, pipelines, and analytics solutions. Establish testing standards for data ingestion, transformation, MDM, and consumption layers. Drive a
shift-left quality mindset , embedding testing early in the development lifecycle. Define test coverage, quality gates, and acceptance criteria aligned with business and regulatory requirements.
Data Quality & Validation: Design and execute data validation frameworks to ensure accuracy, completeness, consistency, and timeliness. Validate master data domains (e.g., Client, Account, Advisor, Product) and downstream analytics datasets. Partner with Data Governance teams to align quality rules with business definitions and stewardship processes. Support reconciliation, controls, and audit requirements for regulated datasets.
Test Automation & Tooling: Build and maintain
automated test frameworks
for data pipelines, APIs, and analytics outputs. Automate regression, smoke, and data quality tests integrated with CI/CD pipelines. Leverage SQL, Python, and data testing tools to validate complex data transformations. Enable automated testing for Databricks, Azure data services, and MDM platforms.
Platform & Integration Testing: Validate end-to-end data flows across source systems, MDM, Databricks Lakehouse, and BI tools. Perform performance, scalability, and reliability testing for large-scale data pipelines. Support UAT by partnering with business users and Product Owners to ensure requirements are met. Assist with production readiness, release validation, and post-deployment verification.
Collaboration & Continuous Improvement: Work closely with Data Engineers, MDM Engineers, Product Owners, and Business Analysts to resolve quality issues. Provide guidance and mentorship to engineers and analysts on quality best practices. Analyze defects and incidents to identify root causes and drive preventive improvements. Continuously improve QE frameworks, tools, and processes.
Technical Responsibilities (Hands-On): Develop data quality and validation scripts using
SQL, Python, and Spark-based frameworks . Validate Databricks Lakehouse solutions built on Delta Lake. Test MDM configurations, matching rules, survivorship logic, and publishing processes. Integrate quality checks into
CI/CD pipelines
and automated deployment workflows. Monitor and report on quality metrics, trends, and risks.
Qualifications: 7+ years of experience in Quality Engineering, QA, or Test Automation roles, with a strong focus on data platforms. 3+ years of hands-on experience testing
data pipelines, data warehouses, or lakehouse architectures . Strong proficiency in
SQL
and experience using
Python
for test automation and validation. Experience with cloud-based data platforms, preferably
Azure and Databricks . Solid understanding of data engineering concepts, data modeling, and ETL/ELT processes. Experience working in Agile delivery environments.
Preferred Experience: Experience testing
MDM platforms
(e.g., Profisee) and master data domains. Experience in
financial services or regulated industries . Familiarity with data governance, data catalogs, and metadata management tools. Experience with API testing and validation of downstream data consumers (BI, reporting, analytics).
Key Responsibilities: Quality Engineering & Test Strategy: Define and implement
end-to-end quality engineering strategies
for data platforms, pipelines, and analytics solutions. Establish testing standards for data ingestion, transformation, MDM, and consumption layers. Drive a
shift-left quality mindset , embedding testing early in the development lifecycle. Define test coverage, quality gates, and acceptance criteria aligned with business and regulatory requirements.
Data Quality & Validation: Design and execute data validation frameworks to ensure accuracy, completeness, consistency, and timeliness. Validate master data domains (e.g., Client, Account, Advisor, Product) and downstream analytics datasets. Partner with Data Governance teams to align quality rules with business definitions and stewardship processes. Support reconciliation, controls, and audit requirements for regulated datasets.
Test Automation & Tooling: Build and maintain
automated test frameworks
for data pipelines, APIs, and analytics outputs. Automate regression, smoke, and data quality tests integrated with CI/CD pipelines. Leverage SQL, Python, and data testing tools to validate complex data transformations. Enable automated testing for Databricks, Azure data services, and MDM platforms.
Platform & Integration Testing: Validate end-to-end data flows across source systems, MDM, Databricks Lakehouse, and BI tools. Perform performance, scalability, and reliability testing for large-scale data pipelines. Support UAT by partnering with business users and Product Owners to ensure requirements are met. Assist with production readiness, release validation, and post-deployment verification.
Collaboration & Continuous Improvement: Work closely with Data Engineers, MDM Engineers, Product Owners, and Business Analysts to resolve quality issues. Provide guidance and mentorship to engineers and analysts on quality best practices. Analyze defects and incidents to identify root causes and drive preventive improvements. Continuously improve QE frameworks, tools, and processes.
Technical Responsibilities (Hands-On): Develop data quality and validation scripts using
SQL, Python, and Spark-based frameworks . Validate Databricks Lakehouse solutions built on Delta Lake. Test MDM configurations, matching rules, survivorship logic, and publishing processes. Integrate quality checks into
CI/CD pipelines
and automated deployment workflows. Monitor and report on quality metrics, trends, and risks.
Qualifications: 7+ years of experience in Quality Engineering, QA, or Test Automation roles, with a strong focus on data platforms. 3+ years of hands-on experience testing
data pipelines, data warehouses, or lakehouse architectures . Strong proficiency in
SQL
and experience using
Python
for test automation and validation. Experience with cloud-based data platforms, preferably
Azure and Databricks . Solid understanding of data engineering concepts, data modeling, and ETL/ELT processes. Experience working in Agile delivery environments.
Preferred Experience: Experience testing
MDM platforms
(e.g., Profisee) and master data domains. Experience in
financial services or regulated industries . Familiarity with data governance, data catalogs, and metadata management tools. Experience with API testing and validation of downstream data consumers (BI, reporting, analytics).
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