AVAILABLE · MAY 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>

See the work 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
CHURN CUT · SIFY
0%
Customer churn reduction via SQL segmentation on 100K+ user interactions.
DECISION SPEED · REHOUZD
0%
Faster stakeholder decisions via Power BI & UAT-validated reporting.
MODEL PRECISION · IEEE
0%
Ponzi scheme detection on blockchain — Snowflake + Python.
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 May 2026. Analytics specialization bridging data engineering and project leadership.
⚡ LEADERSHIP
PMI Club Treasurer
NUPM Finance Coordinator at Northeastern. Managed org budget & financial strategy — drove +70% engagement and 2× cost reduction vs prior cycle.
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
Gathered stakeholder requirements to improve ETL pipelines and documentation on relational data warehouses — applying dimensional modeling and transformation methods that standardized reporting source data. Designed Power BI dashboards communicating analytical and financial insights to cross-functional stakeholders, integrating operational reporting and UAT validation cycles.
−45% discovery time −70% decision time −50% cost/query
→ TX, US
FEB 2023 —
DEC 2024
Data Engineer
Sify Technologies · Chennai, India
Analyzed 100K+ user interactions using SQL window functions, CASE logic, and partitioning. Translated product goals into measurable KPIs by liaising between business, IT, and data teams, applying ETL pipeline orchestration, dimensional modeling, and trending analyses. Drove a legacy infrastructure migration for a global client base executing UAT, technical spec reviews, and data validation to ensure 100% data governance compliance.
−30% churn −35% system cost −76% latency +15% efficiency −25% turnaround
→ IN
OCT 2022 —
FEB 2023
Data Analyst Intern
Trio Infalytics · Chennai, India
Conducted in-depth trending analysis on 50K+ records from multiple data sources — executing data integration and quality validation to surface behavioral and performance patterns. Drove launch of a new system generating $30K in monthly recurring revenue. Led automated Python data workflows and KPI tracking solutions for leadership.
$30K MRR −30% manual −50% decision time
→ IN
03 · 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 Git Snowflake AWS Azure Hadoop Spark Power BI Tableau QuickSight Microsoft 365 Jira
ETL Pipelines Pipeline Orchestration Dimensional Modeling Data Warehousing Data Integration Cohort Analysis Segmentation Forecasting Trending Analysis Performance Measurement Business Reporting Data Validation Blockchain GenAI · Claude
Requirements Gathering UAT Stakeholder Management Risk Management Financial Analysis Technical Documentation Portfolio Management User Engagement Analytics
04 · Projects

Things I shipped.

A growing catalogue. From an algo-trading backtester with AI transpilation to blockchain anomaly detection to early consulting work. Swipe / scroll to browse.

Scroll
No. 01 · 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 / Python strategies into VectorBT-compatible code with a sandbox validation step. ~10–100× faster backtests on simple strategies. Validated for correctness against TradingView.
VectorBT FastAPI Binance API AWS · S3 · EC2 AI Transpile
No. 02 · Research
🔗
Ponzi Scheme Detection on Blockchain
Cross-functional Python/SQL ETL pipelines on Snowflake. Feature engineering, trending analysis, anomaly detection & causal inference on blockchain transaction data. +96% model precision, −23% data latency. Presented at IEEE YESIST12 & ICIEICE23.
Snowflake Python SQL IEEE
No. 03 · Engineering
📦
Inventory Management System
Designed automated ETL pipelines on Azure using SQL, dimensional modeling, pipeline orchestration to integrate inventory data. Built Power BI dashboards tracking usage, trends, demand forecasts. −35% manual effort, +45% planning accuracy.
Azure Power BI SQL IBM
No. 04 · 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
05 · 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 Ponzi Scheme Detection on Blockchain 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.
96%
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

06 · 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
Data Analyst Intern → Data Engineer → Business Analyst → Graduate student. The titles change. The trajectory does not.
→ Oct '22 → Feb '23 → Dec '24 → Jan '26
03
Getting Things Done
30% churn cut. 70% faster decisions. $30K MRR. 96% model precision. These aren't slogans. They're receipts.
→ Sify · Rehouzd · Trio · IEEE
07 · 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.

08 · 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.

Let's build
something undeniable.

Open to Business Analyst, Analytics, and Data roles starting May 2026. I'm at my best translating between the business question and the data that answers it.

srprabu01@icloud.com LinkedIn +1 509 308 3354