Supreeth Kabbin

About Supreeth Kabbin

I’m a Data Engineer with 4+ years of experience building scalable data pipelines, orchestration frameworks, and cloud-native analytics platforms across gaming (Activision Blizzard) and investment data (Capital Group). I specialize in designing reliable data systems that support real-time decision-making, high-volume analytics, and mission-critical business workflows.Recently, I’ve been expanding into AI/LLM engineering—developing skills in retrieval-augmented generation (RAG), prompt engineering, LLM evaluation, agentic workflows, guardrails, and MLOps through the DataExpert.io AI Engineering program. My goal is to bridge modern data engineering with emerging AI systems to help organizations build intelligent, end-to-end data + AI pipelines.Across every team I’ve supported, I’m known for ownership, clarity in execution, and the ability to take ambiguous technical problems and drive them to completion. Whether collaborating with data scientists, platform engineers, or product teams, I care deeply about building systems that are scalable, observable, and easy to extend.If you’re working at the intersection of data engineering, AI infrastructure, or LLM-powered applications, I’d love to connect.

Experience

1. Software Engineer 2, Data

   Capital Group

   Jan 2025 — Present · Seattle, WA, US

   Supporting Clarity, Capital Group’s internal analytics platform powering investment insights across portfolio, issuer, and reference data.Lead data engineering for ~100 production ETL pipelines, improving reliability, observability, and data delivery for investment teams.Built a direct S3 → Dremio ingestion path, improving SLAs by ~2 hours and eliminating upstream API delays.Developed a config-driven orchestration framework (Airflow MWAA, AWS Glue, Terraform) to standardize ingestion patterns and reduce onboarding overhead.Delivered a dynamic DAG generation pilot using DAG Factory + YAML, enabling faster dataset onboarding and event-driven execution.Implemented a post-load data quality layer to surface schema, completeness, and timeliness issues, reducing investigation time from hours to minutes.Currently expanding into AI/LLM engineering through hands-on training in RAG systems, prompt engineering, LLM evaluation, agentic workflows, and end-to-end AI application design. Applying these concepts toward future data quality, orchestration, and metadata-driven automation initiatives at Capital Group.

Education

• University of Southern California

  Master of Science - MS, Computer Science (Data Sciences)

  2018 — 2021

• PES University

  Computer Science and Engineering

  2014 — 2018