Data Engineer

Engineered an LLM-powered healthcare audit pipeline using Generative AI, prompt engineering, and structured and unstructured data processing. Built a multi-tenant healthcare data model for more than 800 review records, improving traceability across clients, agents, audit runs, and analytics.

Orchestrated processing and status tracking for more than 140 audit runs through workflow scheduling, data validation, and pipeline automation. Improved AI-to-manual audit accuracy to 96% through LLM evaluation, prompt refinement, automated testing, and validation workflows.

Implemented anomaly detection, schema validation, and data-quality controls for transcript, metadata, and client datasets. Established Git-based DataOps practices with CI/CD, monitoring, and deployment traceability, and automated 12 versions of JIRA/SLA reporting for operational teams.

Engineered Spark-based ETL/ELT pipelines on AWS EMR using PySpark, Spark SQL, batch processing, and data transformation. Reduced manual processing effort by 90% and consolidated data from more than 12 source systems into Snowflake for analytics and business intelligence consumption.

Designed Snowflake warehouse models using star schemas, snowflake schemas, fact and dimension structures, and OLAP principles. Coordinated production workloads in Apache Airflow with scheduling, dependency management, testing, and monitoring, maintaining 99.5% pipeline reliability.

Modernized more than 10 production pipelines by migrating Spark workloads to Spark 3 and applying query optimization and PySpark tuning. Embedded schema validation, anomaly detection, reconciliation, and metadata management into workflows while partnering with product, marketing, and analytics teams.

Developed Python, SQL, and Databricks ETL workflows for healthcare data ingestion, transformation, and validation. Improved healthcare data availability by 40% through Apache Kafka streaming and Databricks batch processing.

Created reporting-ready healthcare datasets using SQL and dimensional modeling for Power BI and Tableau. Optimized processing across Amazon S3, AWS Glue, and AWS Lambda, reducing infrastructure costs by 30% while supporting dependable ingestion and transformation workloads.

Mapped clinical and administrative requirements to source-to-target specifications, transformation rules, and metadata definitions. Validated Databricks pipelines with schema validation, reconciliation, automated testing, Git-based change management, data lineage documentation, and integration testing.

Developed Python, SQL, and Informatica ETL workflows that combined data from disparate databases, increasing analytical data accessibility by 30%. Unified financial, marketing, and blockchain data domains in Snowflake and Hadoop to create consolidated business datasets.

Scheduled Apache Airflow pipelines with workflow dependencies, validation, and monitoring controls to support reliable revenue and operational reporting. Built Power BI dashboards from curated SQL datasets and business intelligence data models, expanding access to actionable revenue insights by 15%.

Structured Snowflake reporting layers using dimensional modeling, star schemas, and fact and dimension design. Partnered with Finance, IT, and Operations teams to analyze data, document lineage and transformations, and enable self-service analytics for business users.

Completed a Master of Science in Information Technology and Management.