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Ishwori Khanal

@ishworikhanal

Senior Data Engineer building cloud-native lakehouse platforms on AWS, Azure, and GCP to deliver governed, real-time analytics.

United States

Message

What I'm looking for

I’m looking to lead senior data engineering work building governed lakehouse platforms, automating pipelines, optimizing Spark and warehouses, and enabling real-time analytics and compliant AI (RAG) on AWS/Azure/GCP.

I’m a Senior Data Engineer with 5+ years designing cloud-native data platforms across AWS, Azure, and GCP, with deep Snowflake and Databricks expertise. I specialize in lakehouse architecture, Spark-native processing, dbt-based transformation, and HIPAA/SOC 2-compliant governance at scale.

At Visa, I automated 120+ enterprise pipelines on AWS (MWAA, EMR, Glue, Redshift, Kinesis), processing multi-million daily financial transactions. I also drove $180K in annual infrastructure savings through workload tuning and platform optimization.

At Kaiser Permanente, I architected an enterprise analytics platform on GCP using BigQuery, Dataflow, and Cloud Composer, applying a Medallion architecture on Databricks and Delta Lake. I built Vertex AI RAG pipelines for natural-language dataset discovery and improved pipeline performance and latency—cutting processing time by 43% and downstream delays by 47%—by tuning Spark execution, ingestion patterns, and orchestration.

More broadly, I modernized workloads onto Azure Databricks, ADLS Gen2, and Delta Lake at Merck, focusing on scalable ETL/ELT, metadata-driven governance, and reliable distributed processing. I enjoy mentoring teams on Databricks optimization and dbt modeling while delivering trusted, secure data products end-to-end.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Kaiser Permanente

May 2024 - Present (2 years 2 months)

Architected scalable healthcare analytics platforms on GCP, Databricks, Snowflake, Delta Lake, and Apache Iceberg, with governed access to enterprise claims data. Led modernization of ingestion and transformation pipelines, including Spark-native processing, dbt/Airflow orchestration, and Vertex AI RAG pipelines.

Data Engineer

Visa Inc.

Nov 2022 - Apr 2024 (1 year 5 months)

Automated orchestration and dependency management for 120+ enterprise pipelines using Airflow (MWAA). Built distributed batch and lakehouse pipelines with Spark/Databricks and Snowflake/Redshift, including incremental dbt transformations and near-real-time streaming for fraud analytics.

Apache Spark Databricks Pyspark Snowflake Amazon Redshift AWS S3 AWS Glue AWS Lambda AWS Kinesis Lake Formation IAM KMS Cloudwatch DBT SQL Python Kafka Jenkins Terraform Git Docker Parquet Oracle Jira Confluence

Big Data Developer

Merck & Co.

Feb 2021 - Oct 2022 (1 year 8 months)

Assisted in building and modernizing batch ETL pipelines for clinical trial, laboratory, and pharmaceutical datasets using Azure Databricks and Delta Lake. Supported monitoring/troubleshooting, governance (SOC 2), and secure data access while contributing to reporting pipeline automation.

Pyspark Azure Databricks Delta Lake Azure Data Factory azure monitor RBAC SQL Python Parquet Power BI Apache NiFi Apache Spark AWS Kinesis Jira Confluence Microsoft Teams SOC Healthcare Data Governance

Python Developer

Accenture

Sep 2019 - Jan 2021 (1 year 4 months)

Developed backend data processing solutions using Python and SQL to transform high-volume operational datasets into analytics-ready relational structures. Built and optimized database objects and distributed ingestion/aggregation pipelines using SQL Server/Oracle and Hadoop tooling.