Open to opportunities

Sajin Shrestha

@sajinshrestha

Message

Senior Data Engineer building scalable cloud data platforms and reliable pipelines for analytics-driven decisions.

United States

Message

What I'm looking for

I’m looking for a role where I can lead cloud-native data platform work—designing reliable pipelines, optimizing performance and costs, and strengthening governance—while collaborating cross-functionally to deliver trusted, analytics-ready datasets.

I’m a Senior Data Engineer with 8+ years of experience designing scalable data platforms and building reliable data pipelines for large-scale analytics and business decision making.

At Johnson & Johnson, I led the architecture of a healthcare analytics lakehouse platform using Azure Databricks and Snowflake. I built ingestion and distributed transformation pipelines with Apache Spark and automated workflows using Azure Data Factory and Apache Airflow, partnering with data scientists and business teams to deliver trusted, analytics-ready datasets.

Previously at Biogen and LifePoint Health, I delivered batch and streaming ingestion on GCP (Pub/Sub, Dataflow, BigQuery) and AWS (S3, Redshift), enforced governance with IAM and compliance controls, improved data quality with validation frameworks, and supported performance tuning and monitoring. I’m passionate about modernizing legacy systems to cloud-native architectures and building robust data ecosystems that reduce operational cost while improving scalability.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Johnson & Johnson

Feb 2023 - Present (3 years 2 months)

Led architecture of a healthcare analytics lakehouse platform using Azure Databricks and Snowflake, building ingestion and distributed transformation pipelines with Spark. Implemented orchestration, governance, monitoring, and real-time ingestion using Azure Data Factory, Airflow, Lake Formation/Snowflake security controls, and Kafka/Event Hubs.

Databricks Snowflake Apache Spark Azure Data Factory Apache Airflow Kafka DBT Great Expectations Azure Event Hubs

Data Engineer

Biogen

Mar 2020 - Jan 2023 (2 years 10 months)

Built batch and streaming ingestion pipelines on GCP using Pub/Sub and Dataflow, structuring data layers in Cloud Storage and BigQuery for research and operational analytics. Developed ETL/workflow orchestration with Python/SQL and Beam plus Composer (Airflow), and implemented data quality validation, access controls, and monitoring using GCP services.

Google Cloud Pub Sub Google Cloud Dataflow BigQuery Google Cloud Storage Apache Beam Google Cloud Composer Python SQL Great Expectations

Data Engineer

Lifepoint Health

Oct 2017 - Feb 2020 (2 years 4 months)

Assisted in building batch data ingestion and ETL workflows for healthcare operational, patient, and financial datasets using Python, SQL, and distributed processing with Spark/Hadoop. Supported orchestration and optimization with Airflow, Redshift, and AWS S3 while implementing governance, monitoring, and reusable transformation scripts.