Lead IT QA Analyst

Assurant, Inc.

Job Overview

Location

India Virtual

Job Type

Full-time

Full Job Description

📋 Description

• Lead IT QA Analyst at Assurant, Inc. is a critical role responsible for ensuring the quality, reliability, and performance of enterprise business intelligence (BI) and analytics solutions. This position validates complex data pipelines, transformation logic, and reporting outputs to support data-driven decision-making across the organization, directly impacting the accuracy of insights used by business stakeholders.
• The role involves end-to-end quality assurance of BI systems, from data ingestion through semantic layers to final reporting, with a strong emphasis on Kimball dimensional modeling, data reconciliation, and automation of testing processes to enhance release velocity and reduce manual effort.
• Assurant, Inc. is a global provider of specialty insurance and risk management solutions, operating in a highly regulated environment where data integrity and governance are paramount. The IT QA team collaborates closely with analytics engineering, data platform, and business intelligence teams to deliver trusted, scalable, and performant BI solutions.
• The successful candidate will work within a virtual, India-based team that values technical excellence, innovation, and continuous improvement, offering exposure to modern data platforms like Databricks, SQL Server, and AI-augmented testing tools.
• In this role, the Lead IT QA Analyst will deepen expertise in enterprise data validation, gain hands-on experience with Databricks Delta Lake, Spark SQL, ETL/ELT pipelines, and performance tuning, while developing leadership skills through mentoring junior team members and shaping QA best practices across the analytics lifecycle.
• What the person will do day to day:
• Validate enterprise BI solutions aligned to Kimball dimensional modeling, including facts, dimensions, conformed dimensions, and slowly changing dimensions (SCD) handling, ensuring structural and semantic correctness of data models.
• Collaborate with business analysts and subject matter experts to confirm business rules, acceptance criteria, key performance indicators (KPIs), and reconciliation logic for BI/analytics outputs, translating requirements into testable conditions.
• Perform end-to-end QA across the full data lifecycle: ingestion (source systems), transformation (Databricks notebooks, Spark SQL, T-SQL), semantic layer (views, cubes, semantic models), and reporting/consumption (dashboards, exports, APIs).
• Analyze third-party administrator (TPA) client files, compare against expected formats and values, and record variances, discrepancies, and data quality issues for resolution.
• Define, document, and evangelize QA standards, guidelines, and best practices for analytics engineering and reporting teams, promoting consistency and reliability in testing approaches across the organization.
• Leverage AI-powered tools to automatically generate test cases, synthetic test data, and comprehensive test plans, increasing test coverage and reducing manual test creation effort.
• Design and maintain reusable test frameworks focused on:
• Data correctness: verifying row counts, aggregate values, null handling, duplicate detection, and data type integrity.
• Business rule validation: testing KPI calculations, conditional logic, exclusions, thresholds, and derived metrics against specifications.
• Regression testing: ensuring pipeline and reporting changes do not introduce defects in existing functionality.
• Data reconciliation: conducting source-to-target and cross-system checks to validate data completeness and consistency.
• Automate QA activities wherever feasible using scripting, CI/CD integration, and test automation frameworks to reduce manual effort, accelerate release cycles, and improve testing efficiency.
• Validate Databricks-based pipelines, including Delta table integrity, notebook execution, job scheduling, and workflow dependencies, ensuring data is processed correctly at scale.
• Test SQL transformations using Spark SQL and T-SQL, verifying logic, performance, and correctness of queries, views, and stored procedures.
• Perform QA on ETL/ELT patterns, including incremental loads, change data capture (CDC) mechanisms, partitioning strategies, and handling of late-arriving data.
• Validate SQL Server database objects such as stored procedures, views, tables, indexes, and job schedules, ensuring they meet performance, security, and maintainability standards.
• Execute performance testing for Databricks jobs and SQL Server queries, analyzing query execution plans, identifying bottlenecks, and recommending tuning strategies, indexing improvements, and cluster sizing adjustments.
• Provide data-driven recommendations to enhance platform performance, reduce computational costs, improve workload stability, and optimize resource utilization.
• Support production execution by monitoring BI/analytics solution releases, validating post-deployment behavior, and assisting in incident triage when data issues arise.
• Assist in platform upgrades and migrations, including Databricks runtime updates, cluster policy changes, and SQL Server version upgrades, by conducting QA in dev/test environments and providing formal sign-off and documentation.
• Maintain comprehensive QA documentation, including detailed test plans, test evidence (screenshots, logs, output files), defect tracking records, and reconciliation reports for audit and compliance purposes.
• Ensure strict adherence to corporate data governance policies, security standards, change management procedures, and industry best practices in all testing activities.
• Mentor and guide junior QA analysts and engineers in QA methodologies, test automation techniques, data validation patterns, and effective use of testing tools, fostering a culture of quality and knowledge sharing.

🎯 Requirements

• Bachelor’s degree in Computer Science, Information Technology, Data Analytics, or a related technical field.
• 5+ years of hands-on experience in quality assurance, testing, or validation of enterprise data warehouses, BI platforms, or analytics solutions.
• Strong proficiency in SQL (Spark SQL and T-SQL), including writing complex queries, validating transformations, and testing stored procedures and views.
• Proven experience testing data pipelines on modern platforms such as Databricks, including Delta Lake, notebooks, jobs, and workflows.
• Demonstrated ability to design and execute test cases for data correctness, business rule validation, regression, and reconciliation scenarios.
• Experience with test automation frameworks, scripting (Python, Bash, or similar), and CI/CD integration for QA processes.
• Familiarity with Kimball dimensional modeling principles, including facts, dimensions, conformed dimensions, and SCD types.
• Experience working with ETL/ELT tools and understanding of incremental loads, CDC, and partitioning strategies.
• Knowledge of performance testing techniques for big data platforms and relational databases, including query tuning and indexing.
• Excellent analytical, problem-solving, and communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders.
• Proven ability to mentor team members, document processes, and drive continuous improvement in QA practices.

🏖️ Benefits

• Competitive salary commensurate with experience and performance, reflecting the critical nature of the role in ensuring data integrity.
• Comprehensive health, dental, and vision insurance plans for employees and eligible dependents.
• Retirement savings plan with company matching contributions to support long-term financial security.
• Generous paid time off (PTO), including vacation, sick leave, and company-observed holidays.
• Opportunities for professional development, including access to training programs, certifications, and conferences related to data quality, analytics, and QA automation.
• Flexible virtual work environment enabling work-life balance while collaborating with global teams across time zones.
• Exposure to cutting-edge technologies such as Databricks, AI-assisted testing tools, and modern data platform architectures.
• Recognition programs and career advancement pathways for high performers demonstrating leadership and technical excellence.

Skills & Technologies

Apache Spark

Senior

Onsite

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Assurant, Inc.

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About Assurant, Inc.

Assurant, Inc. is a global provider of risk management products and services, headquartered in New York. The company offers extended service contracts, vehicle protection, pre-funded funeral insurance, renters insurance, lender-placed homeowners insurance, and other specialty property and casualty coverage. It partners with lenders, manufacturers, mobile carriers, funeral homes, and property managers to distribute its products primarily in North America, Latin America, and Europe. Founded in 1892 and publicly traded on the NYSE, Assurant focuses on supporting consumer purchases of homes, vehicles, mobile devices, and appliances through underwriting, claims administration, and customer support services.

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