Summary
Overview
Work History
Education
Skills
Projects
Timeline
Generic

Ashish Bhattarai

TX

Summary

  • Cloud Data Engineer with 5+ years of experience designing scalable cloud-native data architectures across healthcare, finance, and enterprise environments using Azure Databricks, AWS, PySpark, and SQL.
  • Specialized in building high-performance ETL/ELT pipelines, real-time streaming solutions, and distributed data processing systems to support analytics, reporting, and business intelligence.
  • Strong expertise in Python, SQL, PySpark, Spark SQL, Kafka, Spark Structured Streaming, and Apache Airflow for large-scale data transformation and workflow automation.
  • Hands-on experience implementing Delta Lake and Medallion Architecture (Bronze, Silver, Gold layers) to improve scalability, governance, and enterprise data reliability.
  • Designed and optimized cloud-native data platforms using Azure (Databricks, Data Factory, Synapse) and AWS (S3, EMR, Glue, Redshift, Athena) for secure and scalable analytics workloads.
  • Implemented enterprise data governance and quality frameworks using Unity Catalog, Collibra, and Great Expectations for metadata management, schema validation, and compliance monitoring.
  • Engineered and tuned PySpark pipelines resulting in a 20% reduction in annual cloud compute costs while improving operational visibility and real-time decision-making capabilities.
  • Built CI/CD and Infrastructure as Code (IaC) workflows using Terraform, Jenkins, Docker, and Git to streamline cloud deployments and production data operations.

Overview

7
7
years of professional experience

Work History

Data Engineer

Molina Healthcare
, TX
08.2024 - Current
  • Architected and developed scalable PySpark and Spark SQL pipelines in Azure Databricks to ingest, transform, and aggregate multi-source healthcare datasets supporting enterprise analytics and reporting initiatives.
  • Designed and implemented Medallion Architecture (Bronze, Silver, Gold) pipelines using Delta Lake, improving data scalability, governance, and downstream reporting reliability across healthcare platforms.
  • Orchestrated cloud-native data processing solutions with Azure Databricks, Azure Data Lake Storage, Azure Data Factory, Synapse Analytics, SQL DB, and Blob Storage, enhancing support for high-volume healthcare analytics workloads.
  • Automated 20+ ETL/ELT workflows using Databricks Workflows and parallel processing techniques, reducing manual operational effort by 40% and improving pipeline reliability.
  • Engineered and optimized distributed PySpark workloads through query tuning, partition optimization, and cluster performance enhancements, achieving significant reductions in annual cloud compute costs.
  • Implemented enterprise-wide data governance frameworks using Unity Catalog and Great Expectations, improving schema validation, metadata management, and data quality monitoring across critical healthcare datasets.
  • Developed and productionized PHI de-identification and PII scrubbing pipelines, ensuring HIPAA compliance and maintaining 99% data accuracy across sensitive healthcare records.
  • Led implementation of real-time healthcare data ingestion pipelines using Kafka and Spark Structured Streaming, improving operational visibility and reducing reporting latency for monitoring dashboards by 50%.
  • Designed and maintained T-SQL tables, views, stored procedures, triggers, and functions supporting secure enterprise analytics and operational reporting systems.
  • Collaborated within Agile teams across sprint planning, deployment cycles, production support, and release management for HIPAA-compliant enterprise healthcare applications.
  • Built and maintained CI/CD and Infrastructure as Code (IaC) frameworks using Terraform, Jenkins, Docker, Git, and GitLab to streamline deployment automation and cloud infrastructure provisioning.
  • Established Spark and Hive-based ETL frameworks, enhancing enterprise data retrieval performance, improving large-scale transformation efficiency, and ensuring operational reporting consistency.

Data Engineer

First National Bank
, OH
10.2020 - 07.2022
  • Built and optimized scalable cloud-native data pipelines using Python, SQL, PySpark, and AWS services to support enterprise banking analytics, financial reporting, and operational intelligence initiatives.
  • Processed and transformed multi-terabyte financial datasets using Spark, Spark SQL, Amazon S3, and Redshift, enhancing enterprise reporting performance and enabling scalable analytics.
  • Automated ETL/ELT workflows using AWS Glue, PySpark, and workflow orchestration tools, reducing manual processing effort by 35% and improving data reliability across banking systems.
  • Designed and maintained high-performance SQL stored procedures, indexes, views, triggers, and functions supporting secure banking transactions and enterprise analytics operations.
  • Architected AWS EMR and S3 Data Lake solutions to process high-volume transactional and operational banking datasets, increasing scalability and ensuring fault tolerance.
  • Assisted in implementing CI/CD pipelines and Infrastructure as Code (IaC) solutions using Jenkins and Terraform, accelerating deployment cycles and improving infrastructure consistency.
  • Partnered with analytics and business stakeholders to develop KPI dashboards and customer behavior analytics, facilitating operational reporting solutions that supported data-informed decision-making.

Data Analyst

Koshi Hospital
, Nepal
05.2019 - 09.2020
  • Developed Python-based analytics workflows using Pandas, NumPy, and Scikit-learn to analyze patient trends, hospital utilization, and healthcare service performance metrics, improving reporting accuracy by 25%.
  • Analyzed 100K+ patient, clinical, and operational healthcare records using Python, SQL, and R to support hospital reporting, operational planning, and data-driven decision-making initiatives.
  • Processed large-scale healthcare datasets using SparkR and distributed data processing frameworks, reducing analytics processing time by 35% and improving reporting scalability across departments.
  • Managed and optimized MySQL databases for patient records, billing information, and operational datasets, enhancing reporting efficiency by 30% and minimizing manual data retrieval efforts.
  • Designed interactive Power BI dashboards for patient demographics, hospital KPIs, and appointment trends, increasing executive visibility into healthcare performance metrics and shortening reporting turnaround time by 40%.
  • Implemented Collibra-based data governance and compliance practices, improving healthcare data quality, metadata consistency, and regulatory compliance while reducing data discrepancies by 20%.
  • Collaborated with healthcare administrators and operational teams to streamline data collection, validation, and reporting workflows, cutting reporting delays by 30% and enhancing operational decision-making efficiency.

Education

Bachelor of Science - Computer Science

Youngstown State University
Youngstown, Ohio

Certificate - Data Analytics

Youngstown State University
Youngstown, Ohio

Skills

  • Data Engineering & DevOps: Apache Airflow, Databricks Workflows, Terraform, Jenkins, Docker, Git, GitLab, CI/CD Pipelines
  • Cloud Platforms & Services: Azure Databricks, Azure Data Factory (ADF), Synapse Analytics, Azure Data Lake Storage, AWS S3, EMR, Glue, Redshift, Athena, EC2, IAM
  • Big Data & Streaming: Apache Spark, Kafka, Spark Structured Streaming, Hadoop (HDFS, YARN), Hive, Sqoop, Delta Lake
  • Programming Languages: Python, SQL, PySpark, Spark SQL, Scala, Shell Scripting, T-SQL
  • Databases & Warehouses: Snowflake, PostgreSQL, MySQL, Oracle, SQL Server, Redshift
  • Data Governance & Quality: Unity Catalog, Collibra, Great Expectations, Apache Atlas
  • Visualization & Reporting: Power BI, Tableau, Looker

Projects

  • Credit Card Fraud Detection: Developed a machine learning fraud detection model using Python, Random Forest, SMOTE, and AdaBoost to identify fraudulent financial transactions and support risk analysis workflows.
  • YouTube Data Analytics Pipeline: Built a cloud-based ETL pipeline using AWS Lambda, S3, Glue, Athena, QuickSight, and Python to process, transform, and visualize large-scale YouTube datasets.
  • Tuberculosis Prediction System: Developed deep learning models using TensorFlow, CNN, VGG16, and Scikit-learn for tuberculosis prediction and medical image classification.
  • Stack Overflow Big Data Analytics: Analyzed large-scale developer datasets using PySpark, Hive, Python, and GCP BigQuery while building classification models and business KPI reporting workflows.
  • Group Attendance System: Developed an automated attendance system using Python, React, TensorFlow, OpenCV, and CNN-based computer vision models for facial recognition and attendance tracking.

Timeline

Data Engineer

Molina Healthcare
08.2024 - Current

Data Engineer

First National Bank
10.2020 - 07.2022

Data Analyst

Koshi Hospital
05.2019 - 09.2020

Bachelor of Science - Computer Science

Youngstown State University

Certificate - Data Analytics

Youngstown State University
Ashish Bhattarai