Senior Analytics Engineer

Agentic AI for enterprise finance — built carefully, with humans in the loop.

• Built and shipped an agentic reconciliation system: LangGraph handles the stateful, multi-step orchestration; agents scan financial data for discrepancies and classify issues (business / data / timing) using historical JIRA context over MCP — an analyst reviews every recommendation before it goes anywhere.
• Published reusable MCP agents through our internal developer portal, instrumented with LangSmith for tracing, cost/latency monitoring and evaluation — catching prompt and retrieval regressions before production.
• Programmatically analyzed 600+ Qlik reports to map dependencies and redundancies, then consolidated them onto modernized data-lake tables — validated end-to-end against general-ledger source of truth.
• Built Python validation frameworks and moved report codebases to Git — version-controlled queries, real code review, faster QA with less manual reconciliation.

Data Analytics Engineer

Large-scale data processing, anomaly detection and ML-driven investigation on petabyte-scale pipelines.

• Engineered distributed PySpark / Spark SQL pipelines over HDFS — 30% faster query execution at petabyte scale.
• Automated Python pipelines feeding real-time Tableau dashboards, cutting manual effort 50%.
• Built anomaly-detection frameworks (Grubbs' test) flagging suspicious activity at 90% precision.
• ML-driven root-cause analysis cut incident resolution time 40%; GDPR-compliant anonymization & encryption throughout.

Data Analyst

Loyalty, campaign and operations analytics on Snowflake & Azure.

• Optimized complex Snowflake / Azure SQL queries — 25% faster response times.
• Booking-prediction models from historical loyalty data at 80% accuracy, informing resource allocation.
• RFM analysis + K-Means segmentation enabled personalized campaigns that lifted retention 20%.
• Automated Spotfire dashboards for leadership — campaign ROI tracking that improved effectiveness 10%.

Student Research Assistant

• Used K-Means to identify distinct user-experience states (Frustration, Flow, Boredom) from quantitative UX data; classified them with SVM, Random Forest, mlogit and kNN — evaluated with AUC, Kappa and hypothesis testing. Data wrangling in R (tidyverse, dplyr).

Project Engineer

• Built SSIS ETL processes across multiple sources and SSRS reports for stakeholders; advanced SQL (stored procedures, parameterized drill-downs) plus schema design with indexes and constraints.