Eugine Peter

At Gainwell, I worked on the Advanced Payment Model (APM) platform supporting Ohio Medicaid’s Comprehensive Primary Care (CPC) and Comprehensive Maternal Care (CMC) initiatives. The project focused on processing healthcare claims, member, and provider data to support attribution logic and performance reporting for value-based care programs. I developed and maintained ETL pipelines in Databricks using PySpark and Spark SQL to ingest, transform, and validate structured healthcare datasets hosted in AWS-backed environments. I wrote complex T-SQL queries and stored procedures in SQL Server to implement attribution rules, performance metrics, and reconciliation logic. The curated datasets were delivered to Tableau dashboards and distributed through an internally hosted reporting portal in CSV, Excel, and PDF formats. In addition to development, I supported production monitoring and portal health checks to ensure data refresh reliability and timely report availability. I worked across Stage and Production environments under HIPAA compliance guidelines, participated in weekly client-facing CPC and CMC review calls, and contributed to standardizing deployments using Databricks Asset Bundles (YAML-based configuration). The role combined hands-on data engineering, SQL development, cloud integration, and operational support in a regulated healthcare environment.

As a Data Engineer at J.P. Morgan Chase, I lead the modernization of on-prem Hadoop systems into a multi-cloud analytics ecosystem spanning AWS and GCP. I design and orchestrate complex ETL and ELT pipelines using AWS Glue, Step Functions, Lambda, and Airflow, enabling seamless ingestion and transformation of large-scale financial, risk, and customer datasets. Leveraging Databricks (AWS) and EMR Spark with PySpark and Scala, I build high-performance data transformations and scalable data warehouse models in Snowflake, Redshift, and BigQuery. My work also involves implementing real-time streaming with Kafka and Kinesis for fraud detection, automating infrastructure through Terraform and Jenkins CI/CD, and establishing robust data governance using Glue Data Catalog and BigQuery Data Catalog. I actively collaborate with analytics and data science teams to deliver curated datasets and Power BI/Tableau dashboards, integrating proactive monitoring via CloudWatch and Stackdriver to ensure reliability, security, and cost-efficient data operations across JPMC’s enterprise data landscape.

As a Data Engineer at Target, I led the modernization of the retail analytics ecosystem by designing and implementing an Azure Synapse-based lakehouse architecture that unified sales, inventory, and supply chain data for enterprise-wide analytics. I developed and orchestrated complex data pipelines in Azure Data Factory (ADF) to ingest data from on-prem SQL, Oracle, and POS systems into Azure Data Lake Gen2, and built transformation frameworks in Databricks using PySpark and Scala to cleanse and enrich large datasets. I architected multi-zone (Bronze, Silver, Gold) Synapse models with Dedicated and Serverless SQL Pools, optimized for performance and cost efficiency, and integrated Power BI for real-time business reporting. My responsibilities also included implementing data governance with Azure Purview, automating deployments through Terraform and Azure DevOps CI/CD, and developing ADX (KQL)-based dashboards for operational monitoring and data quality validation. The solution significantly reduced manual processes and improved data reliability, enabling real-time retail insights across the organization.

At Cardinal Health, I played a key role in the company’s data modernization initiative by migrating legacy on-prem warehouses into Azure Synapse Analytics to build a scalable, compliant, and auditable cloud ecosystem. I engineered secure ETL and ELT pipelines using Azure Data Factory (ADF) and Databricks with PySpark and Scala, integrating more than 200 healthcare and financial data sources into Azure Data Lake Gen2. I implemented Delta Lake architecture for incremental loads and schema evolution, built Synapse Dedicated SQL Pools for analytical modeling, and automated CI/CD deployments using Terraform, Jenkins, and Azure DevOps. I also established data validation and monitoring frameworks leveraging ADX (KQL), Azure Monitor, and Power BI to ensure data quality, governance, and performance across all environments, enabling regulatory compliance and real-time analytics across clinical and financial domains.

As a Data Engineer at PNC Bank, I focused on building advanced analytics and machine learning pipelines to support predictive modeling and financial data analysis. Using Python (Pandas, NumPy, Scikit-learn) and Spark, I developed ML models for customer segmentation, risk analysis, and forecasting, while also creating complex SQL and PL/SQL procedures to manage large transactional datasets. I designed and optimized ETL workflows in Informatica and Teradata, built MapReduce and Spark modules for big data processing on AWS, and implemented robust database models to support OLAP and reporting systems. My work contributed to faster insights, efficient data movement, and improved decision-making across banking and customer analytics functions.

At Amazon, I worked on data management and ETL processes supporting the Weekly Billing Platform, driving accurate financial reporting and automation across multiple business lines. I designed and developed end-to-end data integration workflows using Informatica for ETL, Oracle and SQL Server for data modeling, and PL/SQL for stored procedures and triggers. I collaborated with business teams to gather requirements, built reusable mappings and workflows with dynamic lookups, and optimized query performance for large datasets. My contributions helped streamline billing operations, reduce manual effort, and enhance the reliability of financial data pipelines across Amazon’s global platforms.