I'm a Data / Business Intelligence Analyst who builds end-to-end analytics solutions — from SQL data modeling and ETL through to interactive dashboards that drive decisions. I design star schemas, write performance-minded DAX and SQL, and turn large, messy datasets into reporting that's clean, trustworthy, and business-ready.

My recent portfolio work spans Power BI & SQL (star-schema semantic models, performance-minded DAX, dashboard design), PostgreSQL & Python (a fraud-analytics app on a 6.3M+ row dataset), Excel FP&A (Power Query → Power Pivot → DAX, time-intelligence and budget-vs-actual reporting), and data-warehouse design using medallion (bronze/silver/gold) architecture. I care about the unglamorous parts that make BI trustworthy — idempotent ETL, grain integrity, and numbers that reconcile end to end.

A BI dashboard on 6.3M+ PaySim transactions, built around one mandate: cut fraud-investigation false positives without losing catch rate. The headline finding — a single high-precision rule catches 76% of all fraud at 97% precision, while a second noisy signal inflates flagged volume to 2.5M at near-zero precision — became a concrete recommendation to drop the diluting signal. Live dashboard ↗
Python · PostgreSQL · Streamlit · Data Modeling

A one-click FP&A reporting dashboard in Excel: change a single date cell, hit Refresh All, and every KPI, variance, and chart updates. Power Query unpivots monthly P&L grids into tidy fact tables; a Power Pivot star schema (actuals + budget facts over shared date / account / department dimensions) drives DAX time-intelligence measures — current month, trailing 3 / 6 / 12, and YTD with prior-period and prior-year comparisons. Surfaces budget-vs-actual variance, gross-margin bridges, and operating-expense breakdowns by department and cost type, with KPI tiles and conditional gauges. Includes technical docs and a CFO-style findings memo.
Excel · Power Query · Power Pivot · DAX · FP&A · Budget-vs-Actual

An end-to-end BI case study for a mid-size omnichannel retailer: raw sales, inventory, and movement data modeled through DuckDB + Parquet + SQL into a Power BI executive dashboard with a Kimball-style star schema and a leadership-ready findings deck. The analysis resolved an operations-vs-finance dispute by proving the real risk was overstock, not stockouts — ~$95K of working capital trapped in slow movers (24 of 40 SKUs over 90 days of cover), framed into a 90-day rebalancing plan worth a $50–70K recovery opportunity.
Python · DuckDB · Parquet · SQL · Power BI · DAX

Two linked Tableau dashboards (Sales + Customer) on 9,994 retail transactions (2020–2023), tied together by a shared Select Year parameter that drives every year-over-year comparison. Built around real findings, not just charts: a discount margin cliff at 20% (every tier above it is unprofitable, down to −122.6%), profit concentration far sharper than 80/20 (8.8% of products drive 80% of profit, while 301 products quietly destroy $76.7K in margin), and a 57.7% single-purchase customer tail. Published to Tableau Public.
Tableau · Parameters · YoY Analysis · Profitability · Customer Analytics

📌 More case studies and write-ups are in my pinned repositories below.

• An end-to-end analytics-engineering project on the modern data stack — building production-style ELT and a dimensional model from raw data to tested, query-ready tables. Scope includes:
  • ELT pipelines with full and incremental loading
  • Lakehouse architecture on an open table format (Delta Lake)
  • Apache Spark / PySpark for large-scale transformation
  • Relational + dimensional modeling, including slowly changing dimensions (SCD)
  • Data orchestration with Airflow and Azure Data Factory
  • Modular, tested transformations with dbt on Databricks
• Polishing recruiter-ready BI case studies end to end: SQL model → DAX → dashboard → documentation
• Preparing for DBT Analytics Engineering Certification

Open to roles in Data Analytics, Business Intelligence, and Data Warehousing, where I can contribute through SQL analysis, semantic data modeling, dashboard development, and reliable reporting pipelines.