Devaseenu M

Led ETL test strategy discussions and reviewed test artifacts to ensure comprehensive coverage and complianceProject Overview:

Responsible for migrating large-scale claims, members, and provider data from an on-premises Hadoop ecosystem to Azure Cloud. Rebuilt ETL pipelines using Azure Data Factory and Databricks, converted HiveQL scripts to Spark SQL/PySpark, and migrated historical and incremental datasets to Azure Data Lake. Performed end-to-end ETL testing, data reconciliation, transformation verification, incremental load validation, and ensured compliance with HIPAA standards. Collaborated with data engineers, architects, and business analysts to ensure zero data loss, and optimized pipelines for accuracy and scalability.

Key Responsibilities:

• Reviewed business and functional requirements for claims, member, and provider data.
• Validated source-to-target mappings across SQL Server, flat files, and APIs.
• Led ETL test strategy discussions and reviewed test artifacts to ensure comprehensive coverage and compliance.
• Performed data quality checks, reconciliation, and validation for high-volume datasets.
• Verified transformations, aggregations, and business rules, including eligibility checks, and claim status calculations.
• Tested batch and incremental ETL loads in Azure Data Factory pipelines.
• Executed HiveQL, Spark SQL, and T-SQL queries to validate staging and target tables.
• Ensured data accuracy and consistency in regulatory reports and Power BI dashboards.
• Monitored ETL pipelines for performance, latency, and job failures.
• Documented test plans, executed test cases, and reported defects using JIRA.
• Automated data validation scripts using Python to perform source-to-target reconciliation, data quality checks, and regression testing for large datasets.
• Handled ETL production incidents, conducted root cause analysis (RCA), and ensured timely defect resolution.

Key Achievements:

• Achieved 99.9% data accuracy for claims and member datasets.
• Validated ETL pipelines processing 500+ million records per month
• Optimized Spark jobs and ADF workflows, improving pipeline performance and efficiency.

Project Overview:

Worked on migrating large-scale claims, member, and provider data from legacy Oracle systems to a Hadoop ecosystem. Rebuilt ETL pipelines, converted PL/SQL scripts to HiveQL and PySpark, and migrated historical and incremental datasets. Conducted end-to-end ETL testing, data reconciliation, transformation verification, incremental load validation, and compliance checks. Collaborated with ETL developers, data engineers, and business analysts to ensure data integrity, zero data loss, and optimized Hadoop workflows.

Key Responsibilities:

• Validated source-to-target mappings across Oracle, SQL Server, flat files, and APIs.
• Performed end-to-end ETL testing, including data extraction, transformation, loading, and incremental validation.
• Verified transformations, aggregations, and business rules, such as eligibility checks, claim computations, and risk adjustments.
• Executed complex SQL and HiveQL queries for staging and target table validation.
• Ensured data quality, error handling, logging, and compliance with HIPAA standards.
• Monitored ETL pipelines for performance, latency, and failures.
• Prepared and maintained test plans, scenarios, and defect reports in JIRA.
• Validated Slowly Changing Dimensions (SCD Type 1 & 2) in dimensional data models.

• Completed intensive training on DataStage and ETL fundamentals, gaining hands-on experience with ETL workflows and data integration concepts.
• Developed foundational skills in data extraction, transformation, and loading processes.
• Transitioned from the role due to personal reasons.

• Experienced in validating healthcare insurance and claims processing systems, with a strong knowledge of domain workflows, regulatory compliance (HIPAA), and maintaining data integrity standards.
• Skilled in creating and executing comprehensive test cases, test scenarios, and tracking defects to ensure application quality, accuracy, and seamless functionality.
• Proficient in functional, regression, system, and integration testing, with excellent defect identification, reporting, and clear communication with cross-functional teams.

Project Overview:

Performed end-to-end testing of a large-scale data warehouse, integrating multiple business data sources. Validated ETL workflows to ensure accurate extraction, transformation, and loading of data into the target Oracle database, supporting business intelligence, and reporting requirements.

Key Responsibilities:

• Designed and reviewed test deliverables, including test scenarios, test cases, scripts, and comprehensive test plans.
• Validated ETL workflows in Informatica, ensuring accurate extraction, transformation, and loading.
• Performed database testing on Oracle and Teradata, including complex SQL/PLSQL queries.
• Executed functional, regression, and integration testing; identified and documented defects.
• Managed the Agile story lifecycle, including subtasks, status updates, and defect tracking.

• Provided operational support by assisting with process management and workflow coordination.
• Delivered customer support, addressing client queries, resolving issues, and ensuring timely communication.
• Developed strong problem-solving, organizational, and communication skills applicable across IT and business domains.