Resume
Summary
Senior Cloud Data Engineer with 6+ years of experience designing scalable data platforms, optimizing PB-scale ETL pipelines, and implementing robust data governance frameworks.
Currently transitioning into GenAI Engineering, with hands-on expertise in building RAG systems, vector search architectures, and agentic workflows using LangChain. Proven track record of reducing compute costs by 40% and migrating 50TB+ legacy warehouses to modern Lakehouse architectures on AWS and Databricks.
Core Expertise
- Cloud Data Platforms: AWS Lakehouse, Databricks, Azure Data Factory, Snowflake
- Big Data Engineering: Apache Spark, Delta Lake, Kafka Streaming, Airflow
- GenAI & LLMs: RAG Pipelines, Vector Databases (Pinecone/Chroma), LangChain, Prompt Engineering
- Data Governance: Unity Catalog, IAM, Data Quality (Great Expectations)
- DevOps & IaC: Terraform, Docker, CI/CD (GitHub Actions), Kubernetes
Technical Skills
| Category | Skills |
|---|---|
| Cloud | AWS (S3, Glue, Lambda, EMR, Redshift, Athena), Azure (ADLS Gen2, ADF), Databricks |
| Big Data | Apache Spark (PySpark/Scala), Delta Lake, Hadoop, Snowflake, Kafka |
| GenAI | Retrieval Augmented Generation (RAG), LangChain, LangGraph, OpenAI API, Vector DBs |
| Languages | Python, SQL, Scala, Bash |
| DevOps/Tools | Terraform, Docker, Jenkins, Git, Jira, DBT |
Featured Portfolio Projects
🚀 SQL Server → AWS Lakehouse Modernization
Tech Stack: AWS S3, Glue, Athena, Redshift, Domo
- Designed a multi-track migration strategy moving SQL Server + SSIS workloads to a serverless AWS Lakehouse.
- Implemented Delta Lake for ACID transactions and time-travel on S3.
- Reduced licensing costs by 60% by eliminating legacy database servers.
- View Case Study
🌊 Legacy Hadoop → Databricks Delta Lake Migration
Tech Stack: Databricks, Spark, Azure Data Lake Storage (ADLS)
- Led the migration of a 50TB+ on-premise Hadoop cluster to Databricks on Azure.
- Optimized Spark jobs using partitions and Z-Ordering, improving query performance by 3x.
- Built a unified Unity Catalog governance layer for fine-grained access control.
- View Project
🤖 Enterprise GenAI Knowledge Base (RAG)
Tech Stack: LangChain, Pinecone, OpenAI, Streamlit, Python
- Built a production-grade RAG system to index and search thousands of internal PDF documents.
- Implemented hybrid search (Keyword + Semantic) with re-ranking to improve retrieval accuracy.
- Deployed as a self-service internal tool, reducing information retrieval time from 30 mins to <1 min.
- View RAG Architecture
Professional Experience
Senior Data Engineer
Current Employer | Tempe, AZ
2020 - Present
- Lakehouse Architecture: Spearheaded the design and implementation of a centralized Data Lakehouse using Databricks and Delta Lake, serving 200+ analysts and data scientists.
- Cost Optimization: Refactored inefficient legacy Spark jobs and implemented auto-scaling policies, resulting in a 40% reduction in monthly cloud compute spend.
- Real-time Ingestion: Built low-latency streaming pipelines using Spark Structured Streaming and Kafka to ingest IoT sensor data, enabling real-time anomaly detection.
- Data Quality: Integrated Great Expectations into Airflow DAGs to automate data quality checks, stopping bad data before it reached production dashboards.
Data Engineer
Previous Employer | Location
2018 - 2020
- ETL Pipeline Development: Developed and maintained 50+ ETL pipelines using Python and SQL to transform raw data into star-schema data marts for Tableau reporting.
- Migration: Assisted in migrating on-prem Oracle databases to Snowflake, utilizing Snowpipe for continuous data loading.
- Automation: Automating manual data reconciliation tasks using Python scripts, saving the team 15 hours/week of manual effort.
Certifications & Education
- Databricks Certified Data Engineer Professional
- AWS Certified Solutions Architect – Associate
- Master of Science in Computer Science (Specialization in Data Science)
- Bachelor of Technology in Computer Science