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Ashutosh Khanal

@ashutoshkhanal

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Senior data engineer building secure cloud lakehouse platforms for enterprise analytics.

United States

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What I'm looking for

I’m looking for a Senior Data Engineering role where I can build governed cloud lakehouse/ETL systems, enforce security and lineage, and optimize performance and cost. I want cross-functional collaboration with data science, BI, DevOps, and security teams.

I’m an accomplished Senior Data Engineer with 7+ years architecting scalable, secure, high-performance data platforms across healthcare, finance, insurance, and retail. I specialize in modern cloud native lakehouse architectures that enable enterprise-wide transformation, advanced analytics, and regulatory reporting.

In my recent work on an enterprise healthcare modernization team, I led a payer-centric lakehouse on Azure Data Lake Gen2, Databricks, and Snowflake to unify claims, eligibility, and clinical quality data. I re-architected ingestion into a standardized Bronze/Silver/Gold Delta Lake framework, using Databricks Autoloader, incremental CDC, and dbt—stabilizing daily processing of 120M+ claim records and reducing data freshness delays by 38%.

I’m driven by governance and reliability: I enforce data lineage, security controls, and compliance standards aligned with HIPAA and GDP R requirements through RBAC, Managed Identities, and Azure Key Vault. I also focus on performance and cost—optimizing PySpark transformations (41% faster, 26% lower compute costs) and improving Snowflake spend (32% reduction), while mentoring teams and building observability and CI/CD automation with Terraform and Azure DevOps.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Elevance Health

Apr 2024 - Present (2 years 1 month)

Architected a scalable payer lakehouse on Azure Data Lake Gen2, Databricks, and Snowflake, reworking ingestion into a Bronze/Silver/Gold Delta Lake framework that stabilized daily processing of 120M+ claim records and reduced data freshness delays by 38%. Reduced recurring claims reporting pipeline failures by 45% by redesigning Azure Data Factory orchestration with dependency-aware triggers and d

Azure Databricks Snowflake Delta Lake Azure Data Factory DBT Pyspark Terraform azure monitor Azure Key Vault

Data Engineer

The Hartford

Jan 2023 - Mar 2024 (1 year 2 months)

Designed event-driven ingestion with Kinesis and Lambda, replacing batch-heavy workflows and reducing data latency for fraud detection feeds by 48%. Modernized legacy SSIS workflows into AWS Glue (PySpark) with incremental processing, cutting nightly batch processing time by 43%.

AWS Glue Amazon S3 AWS Lambda Snowflake DBT Amazon Redshift Amazon Athena

ETL Developer

Target

May 2020 - Oct 2022 (2 years 5 months)

Developed and maintained large-scale retail ETL workflows using Informatica PowerCenter and SSIS, enabling centralized transformations for high-volume POS sales, inventory, and promotions. Improved ETL load performance by 35% through Teradata indexing/partitioning and reduced pipeline failures by 28% after re-architecting legacy SSIS batch workflows.

Informatica SSIS SQL Server Teradata DBT Pl SQL Bash Shell

Python Developer

Signify Health

Mar 2018 - Jan 2020 (1 year 10 months)

Built production-grade Python backend services with Django/Flask and performance-aware API design, reducing API latency by 38% through relational database and query optimizations. Implemented asynchronous processing (Celery/RQ with RabbitMQ/Kafka) and improved reliability by reducing transaction failures by 25% using OAuth2/JWT with idempotency and retry logic.