Sairaghu Adepu

About Sairaghu Adepu

Data Engineer with expertise in building scalable, efficient, and impactful data systems across diverse industries including FinTech, E-commerce, Retail, and Healthcare.Currently at Intuit, I design and build real-time data pipelines using Spark Structured Streaming and Kafka, processing millions of conversation events for customer support analytics and AI enrichment services. I work with DynamoDB, Rest APIs, GraphQL APIs, and AWS services to deliver compliant, production-grade data products. WHAT I DO:• Real-time streaming pipelines with Spark Structured Streaming & Kafka• Cloud-native ETL on AWS (S3, DynamoDB, Lambda, Glue, Kinesis)• Data governance & compliance (7216 Tax Data, IDPS encryption)• Kubernetes/ArgoCD deployments for production data workloads• GraphQL API integrations for microservices architecture CAREER HIGHLIGHTS: Built end-to-end Chat Post-Processor pipeline at Intuit processing real-time conversation events with PII handling, call duration analytics, and compliance archival Led optimization of retail pipeline, reducing data shuffling and enhancing Snowflake queries Enhanced data processing workflows for e-commerce giant, reducing processing time by 30% Migrated multiple data systems to AWS, improving system performance by 20% Implemented personalized recommendation systems using AWS and Snowflake for 100k+ daily usersAREAS OF Cloud Data Engineering & Architecture Data Integration & ETL Optimization Data Security & Compliance Real-time Streaming & Data Pipelines Snowflake & AWS Cloud Technologies Data Performance Tuning & Query Optimization TECH STACK:Python | SQL | Scala | Spark | PySpark | Kafka | AWS | Azure | Snowflake | DynamoDB | Hive | Kubernetes | GraphQL | Jenkins | NiFi | Glue | AWS Certified Solutions Architect – Associate AWS Certified Cloud PractitionerOpen to connecting with fellow data engineers, architects, and teams building impactful data platforms!TECHNICAL Languages: Python, SQL, Scala Frameworks: Apache Spark, Apache NiFi, PySpark, AWS Glue, Hadoop CDP/CDH Data Warehousing: Snowflake, Hive Cloud Platforms: AWS, Azure Data Integration Tools: AWS Glue, Azure Data Factory Version Control & CI/CD: Git, Jenkins

Experience

1. Data Engineer

   Intuit

   May 2025 — Present · Mountain View, CA, US

   Architected end-to-end data pipelines using Python, Kafka, and AWS (S3, Lambda, DynamoDB, Kinesis) processing 50M+ conversation records daily with 99.9% reliability• Deployed containerized microservices on Kubernetes (EKS) using Docker and ArgoCD GitOps, achieving 99.99% uptime with auto-scaling handling 5x traffic spikes• Designed DynamoDB schema for conversation entities handling 10K+ requests/second with <10ms p99 latency.• Built real-time streaming architecture using Kafka processing 100K+ events/minute with exactly-once semantics, retry mechanisms, and DLQ patterns• Led SATA deprecation migration validating 100M+ records using Databricks and SQL, achieving 99.9% data parity between legacy and new systems• Enabled scalable, compliant storage of customer support conversations by adding AWS Transcribe-driven speech-to-text and automated PII redaction, supporting 50K+ conversations per day across multiple Intuit products.• Created Gatling load tests and 200+ Karate API automation tests integrated in CI/CD, ensuring system stability under 3K concurrent users• Investigated production issues using Splunk log queries, correlating errors across 20+ services to identify root causes and reduce pipeline failure resolution time• Developed Spark ETL pipelines on AWS EMR to classify and migrate 200M+ legacy meeting events records into compliance-segregated data lake tables for IRS regulatory requirements• Built data validation framework using Databricks SQL to verify 3.7M+ monthly conversations, ensuring data parity between legacy and classified tables with zero data lossContainerized Docker and Kubernetes and leveraging Argo CD for deployment releases environments.• Optimized Spark batch jobs with partition-by-conversation_id strategy and tuned executor configurations for improved performance Technologies: Python, Java, SQL, AWS, Kafka, Databricks, Kubernetes, Docker, ArgoCD, GraphQL, Splunk, Gatling, Karate

Education

• Wilmington University

  Master's degree, Computer Science