Hire me!
Interested in hiring me? Read here to see what I can do!
Thanks for stopping by! I am actively looking for a data analyst, data engineer, analytics engineer, or data science role and available to start immediately. I've spent around 1.5 years building real data systems across multiple companies, working on production data pipelines, marketing analytics at scale, and building systems that real teams actually depended on. I am ready to learn more and contribute to the team and help you achieve your goals.
TL;DR
- Quick to pick up new tools. Every role I've been in came with something I hadn't used before — a new cloud, an unfamiliar stack, a different domain. The ramp-up has been short every time and I've never needed long before I was actually contributing.
- Analytical by instinct. Once the data is in shape, the interesting work starts — figuring out what it's actually saying. I look for the pattern behind the metric, the signal behind the noise, and what a number means for a decision someone actually has to make.
- Cross-domain by experience. My background spans data engineering, analytics, and ML. I've moved between all three depending on what the problem needed — and that range makes it easier to build things that hold up end to end.
- The work ran in production. Real ETL pipelines, automated reporting that replaced manual processes, dashboards that operations teams actually depended on. Not just coursework or demo datasets.
What can I do?
These areas are not siloed — most of my best work sits at the intersection of several. I am up to the challenge of combining them.
Data Engineering
I build pipelines that handle real production loads. At Ideatec, I used Azure Synapse and PySpark to backfill 900M+ rows from on-prem into Azure Data Lake and set up daily ingestion — cutting data latency from days to under 3 hours. At Diggibyte, I built an Azure Data Factory pipeline to pull and flatten nested JSON from a REST API, and configured Delta Lake in Databricks so time-range queries only scan what they actually need, bringing scan volume down from 500 GB to 350 GB. On the orchestration and transformation side, I'm comfortable with dbt for modeling and Airflow for scheduling. I've also worked with Snowflake for cloud data warehousing across academic and personal projects.
Analytics & BI
SQL is the tool I reach for most, and I use it for everything from data quality checks to exploratory analysis. At Fidelity I worked with social media data, marketing mix model outputs, and multi-touch attribution across a large client asset base — the kind of work where the numbers mean something to the business, not just the dashboard. I build in Tableau and Power BI, and I'm comfortable owning the full chain from the query to the view someone else uses to make a call. Outside work, my MBTA on-time performance project used Python and SQL to dig into how transit delays actually pattern across routes and time.
Machine Learning & AI
I've applied ML techniques across different problem types. At Ideatec, I used clustering to perform root-cause analysis on sensor data, pinpointing a dominant failure pattern linked to 72% of equipment breakdowns — then used SQL to project the impact of acting on it. My grad school work includes an LA crime pattern analysis with geospatial modeling and a transit delay prediction model built on 2+ years of MBTA historical data. I'm comfortable with the full workflow from feature engineering to model evaluation.
Location & Work Eligibility
Authorized to work on OPT + STEM extension (up to 3 years) immediately. No sponsorship needed to start. Eligible for H-1B sponsorship thereafter. There is no need for the employer to pay the $100,000 fee to sponsor my H-1B since I am transitioning from F1 to H-1B.
Based in Boston, MA. Open to roles anywhere in the U.S. Open to hybrid or in-office.
Feel free to go through my Work and Projects sections to see what I've actually worked on and built. If something resonates, please reach out.