Senior Analytics Engineer with 8+ years across data engineering and analytics, now building production agentic-AI systems for enterprise finance — MCP-based agents orchestrated with LangGraph, monitored with LangSmith, and kept honest by human-in-the-loop review.
The agents stand on eight years of less glamorous but essential work: large-scale distributed PySpark pipelines, anomaly detection, and applied machine learning. I care most about making enterprise data — structured or not — reliable and usable, whether the consumer is a dashboard, a model, or an agent.
Agentic AI for enterprise finance — built carefully, with humans in the loop.
Large-scale data processing, anomaly detection and ML-driven investigation on petabyte-scale pipelines.
Loyalty, campaign and operations analytics on Snowflake & Azure.
Agents that scan enterprise financial data for discrepancies, classify the issue using historical JIRA context over MCP, and propose a resolution — with an analyst approving every step. LangGraph for orchestration, LangSmith for tracing and evaluation. Less false-alarm triage, faster incident resolution.
Programmatic analysis of a sprawling Qlik estate — extracting metadata and table-level dependencies, finding redundancies via SQL analysis, and consolidating everything onto modernized data-lake tables with end-to-end validation against the general ledger.
Statistical methods (Grubbs' test) applied to flag suspicious user activity at production scale — strengthening data security and pipeline reliability without drowning the team in false positives.
Always happy to talk data platforms, agents — or the messy middle where they meet.