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Mahima Shrestha

@mahimashrestha1

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Senior Data Engineer modernizing cloud lakehouses with Snowflake, optimizing pipelines, and cutting costs.

United States

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What I'm looking for

I’m looking for a senior data engineering role where I can lead cloud lakehouse modernization, optimize Snowflake and pipelines, and own batch + streaming ingestion with strong governance, observability, and cost control—while mentoring engineers.

I’m a Senior Data Engineer with 8 years of experience designing, building, and modernizing enterprise-scale data platforms across healthcare and financial services. I specialize in cloud-based lakehouse and warehouse solutions, with Snowflake as the central analytics platform and PostgreSQL as a key operational and downstream reporting datastore.

Over the past several years, I’ve led strategic migrations from Azure-based architectures (Synapse, ADLS, Azure Data Factory, Databricks) into AWS-native environments, leveraging S3, Glue, Lambda, Redshift, and Kinesis to improve scalability, reduce operational complexity, and strengthen cost governance. In my most recent role, I restructured the organization’s Snowflake environment by optimizing warehouse sizing, implementing clustering and partition-aware modeling, and redesigning ingestion workflows—reducing compute waste and query latency and saving over $2M annually.

I build scalable batch and streaming pipelines using Spark, Airflow, Kafka, AWS Glue, and dbt, ensuring reliable ingestion into Snowflake and strong curated transformation layers using advanced SQL. I bring hands-on expertise in Terraform-based infrastructure, CI/CD automation, performance tuning, and security/governance (including HIPAA-aligned controls), and I enjoy mentoring teams while driving data reliability, observability, and business-aligned outcomes.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

CVS Health

May 2024 - Present (1 year 10 months)

Migrated critical analytics workloads from Azure Synapse/ADLS to an AWS-native architecture (S3, Glue, EMR, Redshift) and transitioned curated datasets into Snowflake, maintaining uninterrupted healthcare reporting. Optimized Snowflake and Spark pipelines—cut compute costs by 20–30%, reduced Spark runtimes by ~30%, and improved pipeline SLA adherence via monitoring, alerting, and resilient orchest

Snowflake AWS RedShift S3 AWS Glue Amazon EMR Apache Spark DBT Apache Airflow Kinesis Terraform

Data Developer

Charles Schwab

Jan 2022 - Apr 2024 (2 years 3 months)

Built enterprise ETL/ELT pipelines for brokerage, trading, and financial reporting using PySpark, AWS Glue, EMR, Lambda, and Redshift. Implemented incremental ingestion and CDC with Kinesis/DMS and tuned Spark/Redshift workloads to improve processing performance by ~25%, improving observability and incident response via CloudWatch/CloudTrail.

AWS Glue Amazon EMR Amazon Redshift Pyspark Amazon Kinesis DMS AWS Lambda Terraform CI CD

Data Engineer

Wellington Management

Oct 2020 - Dec 2022 (2 years 2 months)

Supported development and maintenance of SQL/Python batch pipelines to ingest portfolio holdings, trading activity, and market reference data for investment analytics. Improved data accuracy through cleansing/reconciliation checks, optimized SQL for portfolio dashboards, and reduced dependency on full-refresh loads via incremental processing.

SQL Python ETL Batch Processing Data Cleanse Reconciliations Git Agile Scrum

Backend Developer

ABC Insurance Group

Mar 2019 - Oct 2020 (1 year 7 months)

Developed and maintained Python/Flask backend services for policy management, claims processing, and customer account systems, including REST APIs to integrate with third-party vendors. Optimized PostgreSQL/MySQL query performance and indexing to reduce report generation time by ~20%, and implemented authentication and RBAC to protect sensitive policyholder data.