AVAILABLE · JUL 2026 DATA ANALYST → DATA ENGINEER → BUSINESS ANALYST 📍 SEATTLE, WA

I make data make sense.

I'm Sri Ram Prabu Elenchezhian — a Business Analyst who came up through data: analyst, then engineer. I don't just read the dashboard — I built the pipeline behind it. I turn business questions into data answers, and data into the decisions a business actually makes. datasense>

move your cursor to resolve the noise →
See the work Résumé ⤓ GitHub
Business Analyst Data-Native Requirements → Insights Power BI · Tableau · SQL Stakeholder Management I built the pipeline MS Analytics @ Northeastern Data Analyst → BA
01 · About

A business analyst
with an engineer's hands.

Most analysts can't query the warehouse. I built it. I came up through data — analyst, then engineer — before moving into business analysis, so I sit on both sides of the table: the business question and the SQL that answers it. Based in Seattle. From Chennai.

E
PROFILE
Sri Ram Prabu
Data Analyst → Data Engineer → Business Analyst. I translate between what the business asks and what the data can actually deliver.
Business Analyst Data-Native
SYSTEM COST CUT · SIFY
0%
Lower system cost from a legacy infrastructure migration at 100% governance compliance.
DECISION SPEED · REHOUZD
0%
Faster stakeholder decisions via Power BI & UAT-validated reporting.
MODEL PRECISION · IEEE
0%
Smart-contract fraud detection — 98.2% precision, 95.9% F1 on 3,774 contracts.
MRR GENERATED · TRIO
$0K
Monthly recurring revenue from a new system born of trending analysis.
🎓 CURRENTLY
M.S. Project Management (Analytics)
Northeastern University, Seattle · GPA 3.8 · Expected July 2026. STEM-designated and accredited by the Project Management Institute's Global Accreditation Center. Analytics coursework in R — ALY6000, ALY6010, ALY6015 — plus a capstone built on the Rehouzd and Sify work.
CERTS · AWS CCP · DATABRICKS GENAI · CERTIFIED BLOCKCHAIN EXPERT
⚡ LEADERSHIP
PMI Club Treasurer
NUPM Finance Coordinator for the Project Management Institute (PMI) student chapter. Ran a $1,500 budget ($500 university, $1,000 PMI) across events and vendor costs, reporting to the chapter board — drove +72% engagement and 2× cost reduction vs prior cycle.
🥁 WHERE IT STARTED
Band Captain
Mahatma Schools, Madurai. Led the marching band across 7 institutions on 4 campuses — members, rehearsal schedules, performances — so groups that trained separately could play as one band.
02 · Work

Data analyst to
business analyst.

The deliberate climb: analyze the data, learn to engineer it, then move closer to the decision. Each role earned the next. Numbers verified by the receipts they generated.

JAN — MAR
2026
Business Analyst Intern
Rehouzd · Texas, US · AI-driven real estate platform
Gathered stakeholder requirements and authored BRDs and PRDs, translating business needs into user stories and acceptance criteria, then running UAT validation cycles on operational reports. Designed Power BI dashboards on the company's data warehouse — star-schema models with optimized SQL and DAX — and standardized ETL documentation into source-to-target mappings, so analysts stopped tracing raw tables to answer questions. Mapped the platform's agentic AI workflows for property valuation, lead matching, deal analysis, and rehab cost estimation, documenting each step's data inputs and the points where human review was needed — then wrote roughly 250 test cases against valuation and rehab-cost outputs, checking results against expected values and business rules and logging the failures that drove prompt and workflow fixes. Prioritised requirements in a Jira backlog across Agile sprints so scope was agreed before development. Built conversion funnels and retention views from GA4, Amplitude, and Mixpanel event data and ran A/B tests on features, pinpointing where users dropped off between page view and sign-up. Used Claude to speed up SQL drafting and BRD/PRD write-ups, checking every generated query against source data before use.
−45% discovery time −70% decision time BRDs · PRDs · UAT GA4 · Amplitude · Mixpanel A/B testing Agentic AI workflows ~250 test cases
→ TX, US
JAN 2025 —
JAN 2026
Finance Program Coordinator (Treasurer)
NUPM · Project Management Institute (PMI) Student Chapter · Seattle, WA
Treasurer of the PMI student chapter, running a $1,500 budget — $500 university funding, $1,000 from PMI — and the financial strategy behind it, tracking spending against plan for every programme. Used attendance and engagement data to direct funds toward the events members actually valued, reported financial status to the chapter board, and worked with fellow officers on event planning and vendor costs.
+72% engagement −50% cost $1,500 budget Part-time · Hybrid
→ WA, US
FEB 2023 —
DEC 2024
Data Engineer
Sify Technologies · Chennai, India
Translated product goals into measurable KPIs, liaising with cross-functional business, IT, and infrastructure stakeholders through process mapping and gap analysis. Drove a legacy infrastructure migration for a global client base — server provisioning, capacity planning, and cutover risk management to 100% data governance compliance. Analyzed 100K+ user interactions using SQL window functions and partitioning to identify churn drivers through cohort segmentation and root cause analysis, ranking the drivers with scikit-learn feature analysis and pairing each one with a recommended retention action, which helped cut churn 15%. Engineered PySpark transformations on Databricks to fold ERP, API, and file-based client data — spread across AWS, Azure, and Sify's in-house cloud — into a single analysis-ready layer, orchestrated ETL into Snowflake with Azure Data Factory on incremental loads, modelled it in dbt with tested and documented transformations, and added row-count, null, and uniqueness checks to catch broken loads before they reached business reports.
−34% system cost −26% turnaround 100% governance 100K+ interactions −15% churn dbt · data quality
→ IN
MAY 2022 —
FEB 2023
Data Analyst
Trio Infalytics · Chennai, India
Integrated and validated data from multiple sources to run trending analysis on 50K+ records — surfacing behavioral patterns that drove launch of a system generating $30K/month in recurring revenue. Created star-schema data models and optimised SQL and DAX to keep metrics consistent and dashboards fast as the data grew. Automated Python data workflows and delivered KPI tracking and forecasting for leadership, presenting results in Excel with pivot tables and charts so product owners got early warning of performance declines, and documented the analysis logic and assumptions so other analysts could reproduce, audit, and extend the work.
$30K MRR 50K+ records KPI forecasting Star-schema · DAX
→ IN
NOV 2021 —
JAN 2022
Business Intelligence Consultant
Trio Infalytics · Chennai, India
Met with management and client-facing stakeholders to understand their business goals, then defined the KPIs that would track them. Turned stakeholder questions into data requirements, then delivered written insights and solution proposals for management review, coordinating with internal teams to keep KPI definitions consistent across reports.
KPI definition Stakeholder discovery Apprenticeship · Hybrid
→ IN
MAY 2021 —
JUL 2021
Summer Intern
Trio Infalytics · Chennai, India
Where it started. Processed and organised raw business data in Excel, standardising formats until it was fit for KPI analysis, then built the summaries and charts that gave management a quick read on performance trends. Handled ad-hoc data requests for teams across the business, turning the repeat ones into self-serve reports, and reconciled new reports against source tables during UAT until the mismatches were gone and stakeholders signed off.
Excel · KPI summaries Data preparation Self-serve reporting
→ IN
03 · Education

The years the
timeline skips.

An engineering degree in Chennai, a general management programme taken while working, and an analytics master's in Seattle. Three stops that explain the gaps between the roles above.

EXPECTED
JUL 2026
M.S., Project Management (Analytics Specialization)
Northeastern University · Seattle, WA
STEM-designated and accredited by the Project Management Institute's Global Accreditation Center. The analytics specialization runs in R — exploratory analysis, hypothesis testing, regression, predictive modelling — alongside a project management core in scope, risk, and quality. The capstone ties the two together, built on the Rehouzd and Sify work rather than a case study. Two of the final projects in that coursework are in the rail below.
GPA 3.8 STEM-designated PMI GAC accredited ALY6000 · 6010 · 6015 Capstone PJM 6910
→ WA, US
2025
Advanced General Management Program
IMT Ghaziabad · Ghaziabad, India
Taken alongside the work, to put a business frame around the data. Finance and working capital management, operations and supply chain, sales and distribution, HR and organizational behaviour, and enterprise risk management — the vocabulary stakeholders already speak when they ask an analyst for a number.
Finance & working capital Operations & supply chain Enterprise risk
→ IN
2023
B.E., Computer Science & Engineering
Anna University · India
Where the engineering half came from. Database management systems, data structures, design and analysis of algorithms, probability and queueing theory, discrete mathematics, Python, artificial intelligence, cloud computing, and software engineering — with hands-on labs in SQL and PL/SQL database design, and a Hadoop single-node cluster. The final-year research on detecting smart Ponzi schemes on blockchain was published and presented at IEEE YESIST12 and ICIEICE23.
DBMS · SQL · PL/SQL Data structures & algorithms AI · Cloud · Hadoop IEEE publication
→ IN
ONGOING
Certifications
AWS · Databricks · Blockchain Council
The three that earned their keep: cloud fundamentals behind the AWS work on the trading platform, generative AI fundamentals behind the Claude and Gemini work, and the blockchain credential behind the IEEE research.
AWS Certified Cloud Practitioner Generative AI Fundamentals — Databricks Certified Blockchain Expert
→ CERTS
04 · Stack

Tools I actually use.

No buzzword bingo. Just the kit that's been on production systems and graduate transcripts.

⚡ Python SQL · Window Fns R Java PowerShell VBA Macros Git AWS Azure Snowflake Redshift Databricks · PySpark Microsoft Fabric Hadoop Spark Microsoft 365 Blockchain pandas · NumPy scikit-learn ggplot2 Spark SQL dbt Azure Data Factory TensorFlow · Keras FastAPI Docker
Power BI Tableau Looker QuickSight Alteryx Excel · Power Pivot Claude · GenAI ETL & Orchestration Statistical Modeling Dimensional Modeling Data Warehousing Data Integration Cohort Analysis Segmentation A/B Testing Forecasting Trending Analysis Performance Measurement Business Reporting Data Validation DAX Power Query Semantic Models Hypothesis Testing t-tests · Chi-square · ANOVA Regression · Logistic · Multinomial Ridge · LASSO k-means Clustering VIF · ROC/AUC Churn & Root Cause Analysis KPI Design GA4 · Amplitude · Mixpanel Funnel Analysis Nonparametric Tests
User Stories & BRDs Requirements Gathering Gap & Impact Analysis UAT Agile · Scrum Kanban Jira Power Platform MS Project ServiceNow Stakeholder Management Risk & Change Management Financial Analysis Technical Documentation Portfolio Management User Engagement Analytics PRDs Data Governance Compliance Data Quality Testing Source-to-Target Mapping Claude Code Google Gemini API LLM-Assisted Analysis Agentic AI Workflows AI Output Evaluation Scope · Schedule · Budget
05 · Projects

Things I shipped.

A growing catalogue. From a local-first music-DNA analyzer to R studies on 40,034 gamers and 826 pitchers, an algo-trading backtester with AI transpilation, and blockchain anomaly detection. Swipe / scroll to browse.

Scroll
No. 01 · Audio ML · 2026
🧬
Music DNA Analyzer — 1DNA
Local-first music analytics. Point it at your Spotify, YouTube Music, and Apple Music history and it runs real DSP on every track you've played — BPM, key & Camelot code, LUFS loudness, MFCC timbre fingerprints — then models your taste from those measurements and serves it as a dashboard with k-means mood clusters and harmonic-flow playlist analysis. Crash-isolated parallel analysis made the pipeline ~5.6× faster. Ships with deepcut, a standalone engine that embeds a library with frozen MERT + CLAP so you can retrieve tracks by describing a vibe. 100% local — nothing leaves the machine.
Python · FastAPI librosa DSP SQLite Chart.js Docker Open repo ↗
No. 02 · Statistics · 2026
🎮
Online Gaming Behavior
An end-to-end statistical study of 40,034 online gamers in R, from exploratory analysis through hypothesis testing, predictive modelling, and segmentation. t-tests and chi-square found RPG players significantly likelier than Strategy players to buy in-game (25.3% vs 18.1%). Logistic regression predicted purchases and multinomial regression predicted engagement — login frequency mattered more than session length. Two-way ANOVA tested genre and difficulty against playtime; k-means split players into three archetypes: Casuals, Grinders, Socializers. The findings became monetisation, retention, and churn-prevention recommendations.
R · ggplot2 Hypothesis Testing Logistic · Multinomial ANOVA k-means View study ↗
No. 03 · Predictive Modeling · 2026
⚾
Pitcher Workload & Injury Risk
Merged Lahman career stats, Statcast pitch metrics, and MLB injured-list records into one dataset of 826 pitchers across 48 variables, resolving duplicate names by player ID. Linear and logistic models with VIF diagnostics stripped the multicollinearity — one variable carried a VIF of 43.8 — and isolated cumulative workload as the strongest injury predictor. 70% test accuracy, 88% specificity, AUC 0.682, consistent across train and test. Injury probability climbed from 29% to 88% across the workload range. Built with a team of three.
R Logistic Regression VIF Diagnostics ROC / AUC Team of 3 View study ↗
No. 04 · FinTech · 2025
📈
Algorithmic Trading Platform
Backtesting-as-a-framework: automated ingestion from yfinance & Binance into S3, on-demand backtests on Spot EC2 for cheap compute. Supports candlestick & custom strategies for paper trading. One-shot AI transpilation pipeline converts PineScript strategies into Python, VectorBT-compatible, with a sandbox validation step. ~10× faster backtests. Validated for correctness against TradingView.
VectorBT FastAPI Binance API AWS · S3 · EC2 AI Transpile
No. 05 · Research
🔗
Blockchain Smart Contract Fraud Detection
A CNN classifier in Python (TensorFlow / Keras) built on 3,774 blockchain smart contracts, using hourly, daily, and monthly transaction features to label addresses normal, suspicious, pyramid, or Ponzi, and benchmarked on precision, recall and F1 against a Random Forest baseline. 98.2% precision, 95.9% F1. Presented at IEEE YESIST12 & ICIEICE23.
TensorFlow / Keras Python Random Forest IEEE
No. 06 · BI · 2024
🚕
Uber Review Insights
An interactive Power BI dashboard over Uber app reviews from November–December 2024, cut by sentiment, rating trend, review length, and app version. 67.78% of reviews were positive, and negative reviews ran longer — pointing product teams at the detailed complaints actually worth reading. Date and app-version filters let teams isolate feedback by release and spot rating spikes around promotions or updates.
Power BI Excel Sentiment Analysis Dashboards Open repo ↗
No. 07 · Engineering
📦
Inventory Management System
Power BI demand-forecast dashboards built on SQL dimensional models, integrating inventory and supply chain data into one planning view. +45% forecast accuracy, −35% manual reporting effort.
Dimensional Modeling Power BI SQL IBM
No. 08 · Product · LIVE
✅
Smart Tracker (Focus Space)
A React and TypeScript web app on Firebase for tasks, schedules, and project workflows. The Google Gemini API (2.0 Flash) turns plain-language input into structured tasks, and Zora, a voice-activated assistant, adds, updates, and deletes them hands-free. Kanban boards and priority-based scheduling organise the work, and an analytics dashboard tracks performance, growth trends, and progress over time. The discipline engine this site talks about, shipped as a working product.
React · TypeScript Firebase Gemini 2.0 Flash Kanban Analytics Open app ↗
No. 09 · Consulting
🌐
Royal Automotors — Web
Consultancy project for a local automotive vendor in Madurai. Delivered a web-based attendance management system — an early but useful exercise in translating ambiguous client problems into deployed technical solutions.
HTML Consulting Web
06 · Framework

A framework for
analytics & life.

Intent is the constant. Two lenses shape how it reads: time (when you look) and context (what surrounds it). The same information, across time-frames and circumstances, yields many interpretations — each opening new possibilities. From analyzing across them, patterns emerge. (Perspective comes later — it's a product of how you interpret, not an input to it.)

Intent × Time × Context = { Interpretations } → Possibilities ∴ Pattern
— one intent · many time-frames · many contexts · plural readings
The PM triple constraint · re-read as a personal framework
1h 1d 1w time · when you look promo launch outage context · what surrounds it INTENT the constant · ≈ scope INTERPRETATION time × context · ≈ time POSSIBILITIES branching · ≈ cost PATTERN the signal · ≈ quality
01
i. the input
Intent — the constant.
Everything carries intent. Every dataset is collected for a reason, every system designed with a purpose, every action driven by a why. Universal. Intrinsic.
02
time × context
Interpretation — two lenses.
Intent is read through time (when you look — hourly vs. yearly) and context (what surrounds it — a spike during a promo vs. an outage). Same data, different lens, different meaning. Perspective is what you get out the other side — it depends on the interpretation, so it lives downstream, not here.
03
→ branches
Possibilities — the branching.
Each interpretation opens a different set of futures. One dataset, read seven ways, becomes seven possible decisions worth taking.
04
∴ signal
Pattern — the signal.
Study possibilities long enough — across interpretations, across time — and patterns surface. This is what analysis actually is.
📐 Applied · IEEE-Published Research

The framework, on real work.

Here's how this triangle composed itself across my Blockchain Smart Contract Fraud Detection research — from a single question to a published, validated model.

01 · Intent
"Can we detect fraud in smart contracts?"
02 · Time-frames
Same transactions read at hourly, daily, monthly intervals — each surfacing a different anomaly profile.
03 · Possibilities
Normal · Suspicious · Pyramid · Ponzi — each address branched four ways.
04 · Pattern
Feature engineering + causal inference surfaced the fraud signature.
98.2%
Model
Precision

This isn't a framework I built.
It's one I noticed — first in data, then in everything else.

— sri ram prabu, on the napkin sketch that started this page

07 · Engine

Three quiet verbs
behind every number.

A brand isn't an aesthetic. It's a sequence of small, repeated decisions.

01
Discipline
Pursuing a Master's in Seattle while taking internships across state lines. Showing up for the harder version of the day when an easier one is available.
→ M.S. + Rehouzd internship · 2025–26
02
Consistency
Summer Intern → Business Consultant → Data Analyst → Data Engineer → Business Analyst → Graduate student. The titles change. The trajectory does not.
→ Trio '21–'23 · Sify '23–'24 · Rehouzd '26
03
Getting Things Done
34% system cost cut. 15% churn cut. 70% faster decisions. $30K MRR. 98.2% model precision. These aren't slogans. They're receipts.
→ Sify · Rehouzd · Trio · IEEE
08 · Locale

Filed from the
Pacific Northwest.

Where the work happens. The climate isn't background — it sharpens the output. Mist, mountains, espresso, evergreens.

47°36′ −122°19′ Seattle · Cascadia
--:--:-- PST
MT. RAINIER · 14,411 FT
☕
Third-wave espresso
Seattle invented modern coffee. Three independent roasters within walking distance. The work is caffeinated.
// fuel
🏔️
Cascades & Olympics
Mountains on both horizons. Mt. Rainier visible from campus — when the weather agrees. A scale check against your daily problems.
// perspective
🌊
Puget Sound
Saltwater on three sides. Ferries instead of subways. A different kind of commute and a different kind of patience.
// pace
☔
152 grey days
More mist than monsoon. The constant grey is good for code — fewer distractions, longer focus windows, slower light.
// focus
🌲
Doug fir & cedar
Evergreens everywhere. The trees outlive everyone in the room. They remind you to think in decades, not deadlines.
// long-term
⏱️
Pacific Standard Time
UTC −8. Late overlap with India, early with East Coast, full overlap with the Bay. Built for global collaboration.
// global

Not Silicon Valley. Not New York. Cascadia.
Where the trees are tall, the coffee is dark,
and the work is quiet — until it ships.

09 · Pursuits

What sharpens
the mind.

Two pursuits that run on the same engine as the work — pacing, terrain-reading, recovery. The mountains keep me honest about what actually compounds.

🥾

Hiking — the long view.

// forests · ridges · summits

Trails teach pace. The summit isn't the destination — the rhythm is. Cascades on weekends, Rainier when the weather agrees, Olympics when I want quiet. Every approach is a small project: plan the route, read the terrain, manage the resources, finish before dark.

🏔️ Mt. Rainier · Skyline Trail 5.5mi · 1,700ft
🌲 Olympic NP · Hurricane Ridge all-day · alpine
⛰️ Cascades · Snow Lake 7.2mi · year-round
🌿 Mt. Si · the gym hike 8mi · steep
→ Same engine as data work: read · plan · execute · recover.
🏂

Snowboarding — the line.

// powder · groomers · trees · bowls

Reading powder. Choosing the line. Recovering from the fall fast enough to keep flowing. Stevens for trees, Crystal for bowls, Baker when the season delivers. Every run is a commitment: pick a line, adjust mid-run, trust the recovery.

⛷️ Stevens Pass trees · home mountain
🏔️ Crystal Mountain bowls · big terrain
❄️ Mt. Baker most snow on earth
🎿 Whistler / Blackcomb aspirational trips
→ Same engine as shipping: commit · adjust · recover · repeat.

Both pursuits run on the same engine as the work: pacing, terrain-reading, recovery.
The mountains keep me honest about what compounds —
and what just looks fast.

10 · Volunteer

Time that doesn't
invoice.

Two programs, a continent and a decade apart, running on the same habit: show up, take the logistics, let the thing work.

🤝 PROJECT MANAGEMENT INSTITUTE · PUGET SOUND
General Volunteer — Seattle
The Project Management Institute (PMI) Puget Sound Chapter serves 2,700+ project management professionals across 200+ companies in greater Seattle. I help plan and run its events — networking nights, professional development sessions, keynotes — handling scheduling, registration and attendee comms, venue setup, and day-of operations so things start on time. Planning and stakeholder work, practised outside the classroom.
🌱 NATIONAL SERVICE SCHEME
Volunteer — Madurai
India's student community-service programme under the Ministry of Youth Affairs & Sports, run on the motto "Not Me But You." Community outreach around the school: cleanliness drives, awareness campaigns, environmental work — and planning the running of it with fellow volunteers and programme officers. The teamwork that later turned into Band Captain and the PMI chapter.

Let's build
something undeniable.

Open to Business Analyst, Analytics, and Data roles starting July 2026 · OPT-EAD work authorization. I'm at my best translating between the business question and the data that answers it.

srprabu01@icloud.com LinkedIn +1 509 308 3354