Akshat Shah

About Akshat Shah

I’m a Data Engineer with 3 years of experience building scalable pipelines, cloud-native ETL workflows, and analytics-ready datasets across healthcare and enterprise environments. I work extensively with Python, SQL, Spark, Airflow, AWS, Azure, and Snowflake, and have hands-on experience processing large datasets such as claims, EHR, HL7, and provider data. My work includes optimizing PySpark jobs, designing dimensional models, improving data quality with validation frameworks, and automating ingestion using Glue, ADF, Kafka, and Step Functions.I enjoy turning complex, messy data into reliable, high-impact data products that support analytics, reporting, and machine learning use cases. I collaborate closely with BI and data science teams to deliver curated datasets that power forecasting models, quality measurement, and population health insights. My long-term focus is on roles in Data Engineering and Data Science, where I can continue building efficient, well-governed, and scalable data systems.

Experience

1. Data Engineer

   IBM

   Jan 2025 — Present · US

   Designed and implemented scalable batch and near real-time data pipelines using SQL, PySpark, and cloud-based processingframeworksto ingest and transform 25M+ records daily, improving downstream analytics data availability by 40% and reducing manual interventionacross multiple business units- Developed and optimized complex ETL workflows integrating structured and semi-structured data from10+ enterprisesourcesystems,standardizing transformation logic and reducing reporting preparation time by 35% while improving overall data consistency- Built dimensional data models, curated datasets, and warehouse layers aligned with business reporting requirements, enablingPowerBIand Tableau dashboards to deliver faster insights while improving query performance by 45% for leadership reporting- Collaborated closely with data scientists, analysts, and cross-functional stakeholders to translate business requirements intoscalabledatasolutions, delivering analytics-ready datasets that improved decision-making turnaround time by 30%- Implemented automated data validation, monitoring, and logging mechanisms using SQL and scripting frameworks, proactivelyidentifyinganomalies and reducing production pipeline failures by 25% while improving data reliability- Optimized high-volume SQL queries, indexing strategies, and distributed processing jobs using PySpark, decreasing processinglatencyby38% and improving performance for large analytical workloads handling millions of records- Supported migration of legacy on-prem ETL workflows to modern cloud data platforms, contributing to architecturemodernizationinitiatives that improved scalability and reduced infrastructure maintenance overhead by 20%- Applied enterprise data governance practices including RBAC access controls, data lineage tracking, and audit logging toensuresecuredata access and maintain 100% compliance with organizational data policies.

Education

• Indus University Ahmedabad

  Bachelor of Technology - BTech, Computer Science

• The University of Texas at Arlington

  Master's degree, Computer Science