Himanshu Bhatt

About Himanshu Bhatt

Senior Data Engineer and Cloud Specialist focused on pioneering low-latency real-time data pipelines and integrating advanced graph database (Neo4j/Neptune) technologies. With a background including an M.Sc. in Data Science, I am dedicated to engineering scalable, resilient data infrastructure that translates into tangible business value.My expertise spans the entire data lifecycle, from high-volume batch processing to ultra-low latency streaming:Real-Time & Innovation: Engineered a sub-second, low-latency ingestion pipeline using AWS Kinesis and Apache Flink, reducing processing time to 3-10ms. I also pioneered an Agentic Solution PoC using Prompt Engineering to automate graph merge queries, leading the charge in data integration innovation.Scalable Batch ETL/ELT: Expert in building, optimizing, and orchestrating robust batch data processing workflows using AWS Glue and PySpark on EMR Serverless. I ensure data quality and schema consistency for analytics in Amazon Redshift, including implementing Hive-style partitioning for efficient querying.Business Impact & Cloud Architecture: I\'ve conceptualized and executed comprehensive BI frameworks on AWS (utilizing Glue, QuickSight, Lambda, Step Functions, Redshift) that improved operational efficiency by 30% across multiple internal departments. This work included optimizing TimescaleDB to reduce query time from 3 minutes to 3 seconds.I drive strategic decisions by leveraging a deep technical skill set: Python, SQL, PySpark, AWS Cloud (EMR Serverless, Glue, Redshift, Step Functions), Kafka, Flink, Neo4j, and Power BI. I thrive on transforming complex data challenges into high-performing systems that directly support strategic business outcomes.

Experience

1. Senior Data Engineer

   Bitwise

   Oct 2024 — Present · IN

   Architected a Centralized Data Hub: Engineered a serverless PySpark ETL framework on AWS EMR to ingest and transform data from 30+ disparate source systems into a unified DynamoDB architecture, ensuring a single source of truth for the organization- Organizational Leadership & Data Strategy: Successfully broke data silos across multiple departments by collaborating with cross-functional teams to gather requirements, leading to the creation of a comprehensive, centralized Data Dictionary- Optimized Data Modeling: Designed and implemented high-performance DynamoDB data models using advanced indexing strategies to ensure low-latency, high-speed data retrieval for real-time application queries- Real-Time Ingestion & Performance: Developed a low-latency pipeline using AWS Kinesis and Apache Flink, achieving sub-second record turnaround times (3-10ms) for critical data streams- Infrastructure & Observability: Enhanced system reliability by setting up automated AWS CloudWatch alerts for job failures and completions; implemented AWS Glue Crawlers on the S3 Data Lake to facilitate rapid metadata discovery and ad-hoc analysis- AI & Graph Innovation: Spearheaded a PoC for an Agentic Solution using Prompt Engineering to automate graph merge queries, following a technical evaluation of Neo4j and Amazon Neptune for real-time relationship analysis- Knowledge Graph: Led PoC for a User Activity Knowledge Graph using PySpark ETL to structure raw interaction data, revealing complex behavioral patterns and modeling the Customer Tech Buying Journey. Stored graph data in Neo4j and AWS Neptune for scalable, complex analytics.

Education

• Delhi University

  Bachelor of Science - BS, Instrumentation

  2014 — 2017

• Delhi University

  Bachelor of Science - BS, Instrumentation Technology

• Liverpool John Moores University

  Master of Science - MS, Data Science