Data Engineer Big

ASHRITHA REDDY

Data Engineer

+1-260-***-**** ****************@*****.*** Indiana, USA

Professional Summary

Results-driven Data Engineer with 4 years of experience designing and optimizing large-scale data solutions for healthcare and financial services clients. Skilled in building ETL/ELT pipelines, implementing HIPAA-compliant architectures, and enabling real-time analytics using AWS, Azure, and modern big data technologies. Proven ability to deliver secure, scalable, and high-performing data pipelines supporting advanced analytics, fraud detection, and regulatory compliance. Technical Skills

• Cloud Platforms: Azure (Databricks, Data Factory, Synapse, Data Lake), AWS (Glue, Redshift, S3, EMR, Lambda, Kinesis,CloudWatch)

• Big Data Ecosystem: Apache Spark, Hadoop, Kafka, Hive, Delta Lake, PySpark

• Databases & Storage: PostgreSQL, MySQL, MongoDB, Snowflake, Amazon RDS

• ETL/ELT Tools: Azure Data Factory, AWS Glue, Databricks, Informatica, SSIS, Airflow

• Programming & Scripting: Python (Pandas, NumPy, PySpark), SQL, Scala, Bash

• Data Visualization: Power BI, Tableau, AWS QuickSight

• DevOps & CI/CD: Docker, Kubernetes, Terraform, Jenkins, Git

• Other Skills: Data Quality & Governance, Data Warehousing, Streaming Data, Batch Processing Professional Experience

Elevance Health Data Engineer Jan 2024 – Present Indiana, USA

• Designed and deployed ETL pipelines in Azure Data Factory and Databricks to process healthcare claims data, reducing latency by 20%.

• Built a HIPAA-compliant data lake on Azure Data Lake Storage, integrating structured and unstructured datasets from multiple healthcare systems.

• Implemented real-time ingestion using Azure Event Hubs and Kafka, improving fraud detection response times.

• Developed PySpark transformations in Databricks for batch and streaming data processing, improving throughput of claims validation workflows.

• Created Power BI dashboards for clinical and claims data, providing executives with insights into approval rates, anomalies, and processing delays.

• Automated pipeline monitoring and alerts using Azure Monitor and Logic Apps, reducing downtime and manual intervention.

• Partnered with data science teams to create feature engineering pipelines for predictive modeling (readmission risk, patient segmentation).

• Tuned Spark clusters in Databricks by adjusting partitioning and caching strategies, cutting processing time by 15% on large batch jobs.

• Enforced RBAC and AAD authentication across Azure resources, ensuring compliance with HIPAA and security best practices. JP Morgan Chase Data Engineer Jan 2021 – Dec 2022 Hyderabad, India

• Designed and implemented scalable ETL workflows using AWS Glue and Python to process millions of daily payment transactions.

• Built a centralized data lake on Amazon S3, integrating structured and semi-structured datasets for secure financial data storage.

• Developed real-time streaming pipelines with Apache Kafka and AWS Kinesis, enabling anomaly detection and fraud monitoring.

• Optimized PySpark jobs on Amazon EMR, reducing transaction aggregation time by 30%.

• Deployed Redshift clusters for analytical queries, improving reporting efficiency for compliance and risk management teams.

• Built QuickSight dashboards to visualize fraud rates, chargebacks, and settlement timelines, cutting manual reporting effort by 35%.

• Automated infrastructure provisioning with Terraform and AWS Lambda, improving scalability and reducing operational overhead.

• Implemented data validation frameworks in Python to enforce schema rules, improving data accuracy for reconciliation reports.

• Migrated legacy SQL Server datasets into AWS RDS and Redshift, ensuring 99.9% data integrity and faster analytics.

• Coordinated with compliance teams to meet PCI DSS and SOX regulatory standards, ensuring secure handling of sensitive transaction data. Academic Projects

Cloud-Based Data Lake for Healthcare Analytics (AWS, PySpark, Redshift, Power BI)

• Designed and implemented a cloud-native data lake on AWS S3 to store structured and unstructured healthcare datasets (HL7, claims, patient records).

• Developed ETL pipelines using PySpark and AWS Glue for cleaning, transforming, and loading data into Redshift for analytical queries.

• Built Power BI dashboards to visualize patient admission trends, disease outbreaks, and treatment costs.

• Result: Delivered a HIPAA-compliant architecture that improved data accessibility for healthcare research teams. Education

Master of Science in Information Technology – Indiana Institute of Technology, Fort Wayne, IN Bachelor of Technology in Computer Science – JNTUH, Hyderabad, India Certifications

• AWS Certified Data Analytics – Specialty

• Microsoft Certified: Azure Data Engineer Associate

• Databricks Certified Associate Developer for Apache Spark

Contact this candidate