S01 · E01 The Pilot
Kids…
how i met your Data Analyst. Analytics Engineer. BI Engineer. Data Engineer.

Spoiler alert: that's me. I build dashboards, data pipelines, and BI systems that turn messy data into decisions worth telling stories about. Everything from SQL and Power BI to Python, cloud warehouses, and the occasional LangChain agent. But every good story starts at the beginning...

Start the story →
Paritosh Vyawahare
Paritosh · Boston '26
STARRING Paritosh DATA · BI · ENG
"

Data is the new story. And I'm here to tell the right ones.

T. MOSBY
📍 Boston, MA · Open to full-time roles
Paritosh Vyawahare
Paritosh · Boston '26
For Robin. ♥
The Beginning

Kids, every good story has a beginning. Mine starts back in 2022, in India. Two years of data work. Dashboards at Larsen & Toubro. Power BI, DAX, and product analytics at Aspen Systems. Then in 2024, I packed up my life and moved to Boston for a Master's in Data Analytics Engineering at Northeastern University. Finished it in April 2026.

Now somewhere in the middle of all that, something clicked. I didn't stop being the person who makes the dashboard. I just went deeper. Now I build the dashboard, the pipeline that feeds it, and sometimes the AI agent that can question it. That's why I work across the whole data-to-decision pipeline. SQL, Power BI, and Tableau for the front end. Python, ETL, and cloud warehouses in the middle. LangChain and RAG when the moment calls for it. Same story. Bigger toolkit.

So that brings us to right now. I'm looking for a full-time role in Data Analytics, Analytics Engineering, Business Intelligence, or Data Engineering. If any of those sound like something you're building, well. Let's talk.

True story.
The Timeline, In Icons
2019
Computer Science Engineering
2022
Data Analyst · L&T
2023
Data Analyst · Aspen
2025
Data Analytics · Northeastern
Today
Building Data & BI Systems

but that's a story for another time...

✦  EP. 508  ·  THE PLAYBOOK  ✦

The Playbook.

Every good story has its plays. Here are four of mine. Across BI, analytics engineering, ML, and the AI moments in between. Each one solved a real problem. Each one shipped something you can click.

Play / 01
Data Eng · BI
LATEST

Battery Analytics

An end-to-end manufacturing analytics platform for high-volume battery production.

This one's the newest, and it goes end-to-end. High-volume sensor and production data flowing through a PySpark pipeline into Snowflake, orchestrated with dbt, with automated data-quality checks at every layer. Statistical process monitoring and ML classification flag abnormal behavior before it becomes a defect. On top of it all: a self-service Plotly Dash dashboard for the operators, and a RAG assistant that reads unstructured failure reports for root-cause analysis. Snowflake for the warehouse. dbt for the transforms. Marshall-level thorough.

PythonPySparkSQLSnowflakedbtAWS S3Plotly DashRAG
GitHub →
Play / 02
Data Eng · BI

Policy Lapsation

End-to-end ETL and BI for life insurance lapse analysis. 90K policies, 6+ KPIs.

Talend pipelines into a PostgreSQL warehouse, dashboards in Power BI and Tableau. Surfaces lapse rate by policy age, premium band, demographics, and geography, designed for the retention team to act on, not just look at.

TalendPostgreSQLPower BITableauSQLETL
GitHub →
Play / 03
Data Analyst · AI-powered

Vera.AI

The AI data analyst that only speaks in numbers.

Upload a CSV, and Vera hands back a full statistical profile, an AI-written analyst report, auto-generated Plotly charts, and a chat where you can ask her anything. Four coordinated agents under the hood: Analyzer, Report, Chart, and Q&A. Every claim traceable to the data. No hallucinations. No made-up numbers.

PythonStreamlitLangChainGroq · Llama 3.3 70BPandasPlotlyDocker
Try Vera live → Watch walkthrough → GitHub →
Play / 04
Machine Learning

Churn Prediction

A predictive model that identifies at-risk customers and drives retention.

Random Forest classifier on 60K customer records, hitting 86% accuracy after feature engineering, hyperparameter tuning, and cross-validation. Identified the real churn drivers: month-to-month contracts and mailed-check payments. Predicted who'd leave. Like Ted, but useful.

Pythonscikit-learnRandom ForestpandasFeature Engineering
GitHub →

Nothing good happens after 2 AM.
Except maybe deploying to prod.

S01 · E03  ·  THE STACK

Marshall's Graphs.

If Marshall taught us anything, it's that any answer sounds smarter with a pie chart behind it. So here's the stack, quantified.

How I Spend My Time
based on 100% of a made-up week
BI & Dash 35% Python / SQL 30% Data Eng 20% AI & ML 15%
Comfort by Tool
on a scale of "used it once" to "I dream in it"
Python SQL Power BI Tableau Snowflake high meh low ← more time spent

Languages

  • Python
  • SQL
  • R

BI & Analytics

  • Power BI
  • Tableau
  • Looker
  • DAX
  • Power Query
  • Qlik Sense
  • Excel

Data & Cloud

  • Snowflake
  • dbt
  • PostgreSQL
  • ETL / Talend / Alteryx
  • AWS
  • Azure Data Factory

AI & Automation

  • LangChain
  • LLMs (Groq, OpenAI)
  • RAG & Vector DBs
  • Prompt Engineering
  • scikit-learn
And next? Well kids, legen... wait for it ...dary things:
dbt, Snowflake, Airflow, and deeper agent orchestration.
Suit Up
Barney would approve.

The Journey.

Every good story has a suit-up montage. This is mine. Three roles. Two countries. One very long flight to Boston.

Jun 2025 to Present
Boston, MA
Data Analytics Engineer
Northeastern University Research Enterprise Services
Apr 2023 to Dec 2023
India
Data Analyst
Aspen Systems
Jun 2022 to Dec 2022
India
Data Analyst Intern
Larsen & Toubro
The Next Chapter
Loading… (that's where you come in.)
Have you met Paritosh?

So that brings us to the end of the story. Well, actually the beginning of the next one. I'm open to full-time roles in Business Intelligence, Data Analytics, Data Engineering, and BI Development. Say hi. First round's on me.

LinkedIn GitHub Email me
P.S. True story.
Let's build something legendary together. P.
Challenge
Accepted.