Technical Data Analyst at Addepar, Pune — building data pipelines, analytics tools, and AI-assisted workflows for financial data. 4+ years across financial services, collaborating daily with Data Solution Consultants (DSCs), clients, and cross-functional teams to deliver production-ready data solutions.
A bit about who I am and what drives my work
I'm a data analyst who sits at the intersection of financial data operations and analytics engineering. My day-to-day involves handling complex client data migrations, building Python tools that automate what used to be manual, and making messy real-world data usable for wealth management platforms. I work closely with Data Solution Consultants (DSCs), clients, and cross-functional teams — translating business requirements into reliable data pipelines and bridging the gap between operations and delivery.
I believe the most valuable skill isn't knowing every function by heart — it's knowing what to build and why, then figuring out the how. I use AI, code assistants, and the internet as tools, the same way engineers use IDEs and documentation.
Outside work: music team head in college cultural group Rangbhumi, occasional interviewer at Addepar, and always up for a conversation about cricket or financial markets.
Tools and technologies I work with regularly
Where I've worked and what I've built
Personal projects built end-to-end — data, ML, dashboards, and deployed apps
Reconciliation tool for comparing two financial datasets - configurable join keys, per-field tolerance thresholds (absolute, %, basis points), break severity ranking, and a downloadable 8-sheet Excel report. Three pre-built use cases: positions, prices, transactions.
Data pipeline for three financial datasets with configurable quality checks (nulls, range, schema, cross-field, duplicates), transformation with derived metrics, and SQLite storage. Dashboard tracks run history, pass rates per dataset, and lets you explore the processed data.
Data quality tool for financial client onboarding files. Upload any Excel file and get 12 automated checks covering schema, identifiers, formatting, and business rules, with a readiness score and downloadable report.
NLP pipeline for Hinglish (code-mixed Hindi-English) text. Handles token-level language detection, transliteration normalisation, TF-IDF features, and Logistic Regression classification across 3,000 social media comments.
Python pipeline on 5,000 simulated batch job logs: SLA breach detection, error code classification via Regex, job instability scoring (60/40 weighted formula), and an interactive dashboard. Directly inspired by real monitoring work at FIS.
End-to-end sentiment classification on 3,000 tweets across 5 topics using TF-IDF + Logistic Regression. 95% test accuracy, 0.997 ROC-AUC, 5-fold CV validation. Full evaluation including confusion matrix and top discriminative features per class.
Open to interesting conversations, collaborations, and the right opportunities
I'm always happy to connect with people working on interesting data problems — especially in financial services, analytics, or anything involving Python and messy real-world data.
Based in Pune, Maharashtra. Open to roles in Pune, anywhere in India, or globally — remote or relocation.