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Sabina Gurung

@sabinagurung

Senior Data Engineer building cloud-native lakehouse platforms and AI-enabled data pipelines that drive scalable, reliable analytics.

United States

Message

What I'm looking for

I’m looking for a senior data engineering role where I can build cloud-native lakehouse platforms, deliver secure batch/streaming pipelines, and apply Data Quality/Data Observability plus GenAI (RAG, semantic/vector search) to improve reliability and accelerate analytics.

I’m a Senior Data Engineer with 7+ years of experience designing, developing, and optimizing enterprise-scale data platforms across insurance, healthcare, retail, and financial services. I build cloud-native solutions across Microsoft Azure, Google Cloud Platform (GCP), and AWS, with deep hands-on expertise in Databricks, Apache Spark, PySpark, Snowflake, BigQuery, Microsoft Fabric, and modern Lakehouse architectures.

In my recent role, I architected a GCP-native enterprise data platform, migrated 50+ legacy AWS ETL pipelines (reducing infrastructure costs by 25%), and delivered scalable ELT pipelines processing 8TB+ daily (reducing processing times by 35%). I also implement Data Quality Frameworks and Data Observability to improve accuracy and cut incident detection/resolution time by 35%, and I integrate Generative AI-enabled workflows using Azure OpenAI, Vertex AI, and RAG for automation that reduces manual effort by 50%+.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Metlife

Aug 2024 - Present (1 year 10 months)

Architected a GCP-native enterprise data platform with BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage, supporting insurance policy, claims, underwriting, and actuarial analytics. Migrated 50+ legacy AWS ETL pipelines to GCP, cutting infrastructure costs by 25%, reducing daily processing time by 35%, and improving data accuracy by 40% through data quality and observability initiatives.

Google BigQuery Google Cloud Dataflow Google Cloud Dataproc Pub Sub Lakehouse Architecture Apache Spark Data Quality Data Observability Vertex AI Retrieval Augmented Generation (RAG)

Data Engineer

Johnson & Johnson

Sep 2022 - Jul 2024 (1 year 10 months)

Designed and built Azure-based batch and near real-time data pipelines for clinical research, patient safety, manufacturing, and commercial datasets. Reduced duplicate processing efforts via a Medallion Lake architecture and improved performance by 40% while cutting manual orchestration work by 50%, with automated data quality and observability reducing production incidents by 30%.

Azure Databricks Azure Data Factory Pyspark Apache Kafka Azure Event Hubs Medallion Architecture Snowflake

ETL Developer

Walmart

Nov 2021 - Aug 2022 (9 months)

Developed and maintained enterprise ETL pipelines with Informatica PowerCenter to integrate high-volume retail transaction, inventory, merchandising, and supply chain data. Improved batch runtimes by 40% through SQL performance tuning and reduced manual intervention by 30% using Unix/Linux shell automation, while enhancing reliability via workflow recovery and production support.

Informatica Oracle PL SQL SQL Tuning Batch Processing Production Support Data Warehousing

Backend Developer

Bank of America

Jul 2019 - Oct 2021 (2 years 3 months)

Built and maintained backend RESTful APIs using Python and Flask for customer account management, transaction processing, and internal banking applications. Improved data accuracy by 25% through validation/reconciliation modules and increased API response times by 35% via database query optimization, while supporting reliable deployments through CI/CD with Jenkins and Git.

Python Flask RESTful APIs Data Validation