Tushar · Data Engineer

$ git log --oneline --graph

How I got here

Five years of watching production billing pipelines fail quietly set the direction. Every stop since has been about building the tools that catch a failure before it becomes an outage, not just cleaning up after one.

7f3a2c1 Accenture · Jun 2019 – Feb 2022

fix: automate batch bill-run workflows, 1 day → 2 hours

First real lesson in production data: a 4.5M-customer billing base doesn't forgive manual steps. Resolved 50+ recurring data quality issues and cut processing time 12×.

c9e81b4 Oracle Corporation · Feb 2022 – Dec 2024

feat: validate telecom billing at scale, 600K+ subscribers

Moved from fixing issues after the fact to designing the checks that catch them first — reconciliation reports and anomaly rules holding 98%+ accuracy within SLA. Earned "Best Upcoming Talent," FY23 Q3.

a10d5e7 Penn State Research · Sep 2025 – May 2026

feat: resume-matching workflow + compliance engine, 96% accuracy

Went back to build the theory behind the practice: a TF-IDF → SBERT → neural refinement matching workflow for candidate screening, and a deterministic rule engine auditing AI fall-monitoring technology into an immutable SQLite audit trail.

f42b901 Barton Malow · May 2026 – Aug 2026

feat: cost analytics + DQ framework shipped to production

Where the two threads met: $146K in platform spend governed, 211 automated quality checks live across 23 tables in a $159B CRM pipeline.

The throughline: every role added a layer — fixing, then preventing, then modeling, then governing. The Barton Malow work is what all four look like combined into one pipeline.

$ cat PRINCIPLES.md

The pragmatic builder

Everything I build is grounded in a production mindset.

"Understanding existing systems deeply before changing them, designing for the next person who has to maintain my work, and communicating technical findings in a way non-technical stakeholders can act on." That’s the standard I actually build to.

01

Never breaks the upstream job

Every data quality and cost pipeline I've shipped runs as a soft-fail observer: a failing check writes a finding row, it never raises and kills the parent job. A DQ failure is a data observation, not a pipeline outage.

→ see it in Cost Analytics 02

Idempotent by design

Every gold table load pairs with a post-write uniqueness check that runs on every execution, not just at build time, so reruns, backfills, and late-arriving data can't silently create duplicate rows.

→ see it in Cost Analytics 03

CI-gated, AI-assisted

Every pipeline repo ships with lint + a schema validator tested against real broken input: a malformed rule or a broken job dependency fails the build before it fails in production. I use Claude Code across the full git lifecycle, including branching, commits, PR reviews, and merges, to move faster, but every change still ships through a branch, a pull request, and a green CI check before it touches main. AI accelerates the workflow; it doesn't bypass the gate.

→ see it in DQ Framework 04

Documented for the next person

Architecture docs, decision logs, and known-limitations sections ship with every pipeline, not just the happy path. The goal is that someone else can maintain it without asking me first.

→ see it across Experience

$ cat experience.log

5+ years turning messy real-world data into decisions leadership can act on

Data Science Intern

Barton Malow Featured

May 2026 – Aug 2026 Michigan, United States

Built a Databricks Cost Analytics pipeline and 4-tab leadership dashboard from Unity Catalog billing/compute data, surfacing $146K in lifetime platform spend and $13.2K in idle resource cost across 885 unused objects. Automated an ownership-resolution hierarchy across 933 resources that cut the unowned-spend gap from 85.6% to 54%.
Designed a Claude AI cost-attribution dashboard isolating agent query costs from shared Databricks serverless compute (20+ users, 8,000+ queries, $0.53 avg cost/query), revealing AI's share of warehouse spend grew 5.5× in 5 months (1.5% → 8.3%), giving leadership real-time cost visibility before it became a budget concern.
Engineered a 211-rule automated data quality framework spanning 23 production tables in a $159B CRM pipeline, surfacing that ~84% of active records carried an issue and root-causing a 39% completeness bug that was silently causing dashboards to underreport, while achieving 100% clean execution using DQX's AI-driven profiler to accelerate future rule generation.

DatabricksETL/ELTCI/CDPySparkSQLPythonClaude CodeGitHub Actions

Cost Analytics repo → DQX repo →

Research Assistant, Data Science

Pennsylvania State University

Sep 2025 – May 2026 Pennsylvania, United States

Engineered a three-stage matching workflow (TF-IDF → SBERT → Neural Refinement) processing 120 resumes against 2,277 projects for RA candidate screening, improving match quality 122% and reaching 96% accuracy.
Architected a deterministic rule engine auditing MyRaina's AI fall-monitoring technology, ingesting activity logs through 5 CMS-mapped checks into an immutable SQLite audit trail, explained via a local Llama 3 layer.

SBERTPyTorchData WorkflowsSQLitePythonSQL

Staff Data Consultant

Oracle Corporation

Feb 2022 – Dec 2024 Bangalore, India

Validated end-to-end billing data pipelines for 600K+ telecom subscribers using SQL-driven quality checks, anomaly detection rules, and reconciliation reports, maintaining 98%+ accuracy within SLAs.
Built and automated KPI dashboards (churn, usage patterns) with 99% on-time delivery, enabling leadership to track performance in weekly reviews.
Led onsite test efforts for a 15-member team across a 1-month go-live window, building client trust by tracking and presenting a declining post-launch bug trend.

PythonSQLOracle DatabaseData AnalysisBusiness Analysis

Award Oracle "Best Upcoming Talent," FY23 Q3

Data Consultant

Accenture Solutions Pvt Ltd

Jun 2019 – Feb 2022 Pune, India

Identified and resolved 50+ recurring data quality issues in production billing datasets for a 4.5M-customer base, improving billing accuracy and customer trust.
Automated batch bill-run workflows using SQL scripts and scheduling, cutting processing time from 1 day to 2 hours (12× faster) and manual effort by 92%.
Designed a SQL-based data validation framework (row-level checks, reconciliation, exception flags) that reduced discrepancies by 60% and shortened month-end close by 10%.

PythonSQLShell ScriptingData AnalysisExcelClient Communication

Award Accenture "Shared Success Catalyst," Addressing Client & Community Needs

$ ls ./projects

Real systems, real numbers

211checks

23tables

84%records flagged

Databricks Data Quality Framework

Automated data quality monitoring built on Databricks DQX. Surfaced that 84% of active records carried at least one issue, and root-caused a ~39% completeness gap in a core reporting table.

CI: lint + schema validation Soft-fail: never breaks the upstream job

DatabricksPySparkSQLPython

View repo →

$146Kspend governed

54%unowned spend (from 85.6%)

2×error caught pre-ship

Databricks Cost Analytics & FinOps

FinOps cost-attribution pipeline on Unity Catalog system tables. Cut unowned spend from 85.6% to 54% and caught a 2x measurement error in the AI-cost methodology before it shipped.

CI: lint + job-config validation Idempotent: 0 duplicate rows on every rerun

DatabricksPySparkSQLStar Schema

View repo →

Scholar Swipe: Scholarship Scraper

Automated scraping pipeline extracting scholarship listings into a centralized PostgreSQL (Supabase) database, with upsert-based dedup and automated summarization.

PythonBeautifulSoup4PostgreSQL

View repo →

Deep Dive · Barton Malow, 2026

Databricks DQX (Data Quality Extended) Implementation

Problem

23 production tables in a $159B CRM pipeline had no automated way to catch bad data before it reached downstream reports.

Why

Bad records silently corrupt reporting and decisions. The earlier one is caught, the cheaper it is to fix.

How

211 automated rules run on a scheduled bronze/gold pipeline, CI-gated and soft-fail, routing findings to record owners every week.

211automated checks in production

23production tables covered

~39%completeness gap root-caused

100%clean execution across the $159B pipeline

This is where principle 03 (CI-gated, AI-assisted) shows up in practice: lint and a schema validator, tested against real broken input, gate every change before it ships to production.

Deep Dive · Barton Malow, 2026

Databricks Cost Analytics pipeline

Problem

85.6% of Databricks platform spend had no identifiable owner, and AI agent costs were invisible inside shared compute.

Why

You can't govern spend you can't attribute, and AI usage was growing too fast to stay unmeasured.

How

An automated ownership-resolution pipeline across 933 resources, plus a dedicated AI cost-attribution layer, feeding a 4-tab leadership dashboard.

$146Klifetime spend surfaced

$13.2Kidle cost, 885 unused objects

2×measurement error caught before it shipped

This is where principles 01 (soft-fail) and 02 (idempotent) aren't abstractions: the gold table load pairs with a uniqueness check on every execution, and a failing DQ rule anywhere upstream writes a finding row instead of killing the job.

$ cat research.md

Published work and competitive recognition

Published Paper · Springer Nature

Autonomous Multi-Agent Governance: AI Sentinel Framework for Mitigating Psychological Restraints in AI-Driven Fall Management

Accepted and presented at SEET 2026 (2nd International Conference on Software Engineering of Emerging Technologies), Penn State Behrend, PhD Research Track. Listed on page 76 of the conference abstract book. Now published in the official conference proceedings by Springer Nature (CCIS vol. 3126).

Read on Springer → View conference →

Competition · Penn State Nittany AI Challenge 2026

AI Sentinel: Top 10 of 60+ Teams

Won the Prototype Phase and MVP Phase, advanced through funded phases to a Top 10 finish at the Pitch Contest, Hintz Family Alumni Center, University Park.

Award · Oracle Corporation

"Best Upcoming Talent" (FY23 Q3)

Recognized for exceptional contributions and high-impact performance on the Telia Company billing analytics engagement.

Leadership · Penn State University

Student Senator

Representing students in discussions with university leadership; championing AI literacy and equitable AI access across Penn State's Commonwealth Caucus.

$ cat skills.yaml

Tools I actually ship with

Programming

Python (Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, TensorFlow, PyTorch, Keras) · SQL · R · Java

Data Engineering & Cloud

Databricks · Delta Lake · CI/CD · ETL/ELT · PySpark · Unity Catalog · Azure · AWS · Oracle Database · PostgreSQL · PL/SQL

ML & Statistics

Regression · Classification · Clustering · NLP · Time Series Forecasting · Feature Engineering · Model Evaluation (AUC-ROC, F1)

BI & Visualization

Tableau · Power BI · Plotly · Matplotlib · Seaborn

Dev Tools

Git · GitHub Actions · Jupyter · Jira

AI Tools

Claude Code · Claude CLI · Claude Cowork · Claude Skills · Claude Projects · Claude MCP · GitHub MCP · ChatGPT · GitHub Copilot · Cursor IDE · Perplexity · Ollama · Google Gemini · Julius AI

$ cat education.log

Background

Pennsylvania State University

Aug 2025 – Dec 2026

Master of Data Analytics · GPA 3.96/4

Statistical Analysis, Data-Driven Decision Making, Predictive Analytics, Deep Learning, Natural Language Processing.

KIIT University

May 2015 – May 2019

B.Tech, Electronics & Instrumentation Engineering · GPA 3.3/4

Control Systems, Digital Electronics, OOP, Data Structures & Algorithms, Web Technology, Artificial Intelligence.

$ mail tushar

Let's build something that ships.

Open to full-time Data Engineer roles starting Dec 2026.

tus2025snoop@outlook.com Download Resume

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