About
I am a data engineer in Melbourne, currently studying a Master of Data Science at Swinburne University. Before that I spent over two years as a customer insights analyst and two years in product operations. My background sits between the commercial and the technical: an MBA in marketing and business analytics, then years of working close to the data, from product catalogues to SQL reporting.
The work I enjoy most is the unglamorous part: getting a messy extract into shape, agreeing what a metric actually means, and then putting it in front of someone in a form they can use. I care about clean assumptions and about being able to rerun something six months later and get the same answer.
How I got here
I started in product operations. Two years at PowerWeave in Mumbai looking after catalogue data and order workflows for a United States wholesale platform taught me to respect a clean spreadsheet, mostly by showing me what a dirty one costs. I then moved to Bangalore for an MBA in marketing and business analytics, and while studying joined KeyGap Software Solutions as a customer insights analyst, where the work became analysis full time: SQL pulls, Tableau dashboards, A/B test reporting, and the weekly KPI rollup for the team lead.
Somewhere in that job I noticed I was more interested in the data questions than the campaign ones. Which segment is actually growing. Which signup cohort sticks around. What the dashboard is quietly leaving out. More often than not, the answer sat upstream, in how the data was pulled, joined and shaped. So I moved to Melbourne and started the Master of Data Science to build the technical foundation properly, and data engineering is the work I want to do with it.
How I like to work
Ask what decision the number is for, agree the definition before building anything, fix the splits before anyone models, keep the workings reproducible, and write the thing down so the next person is not starting from scratch. A report that nobody changes their mind because of has not really finished its job, and neither has a pipeline that nobody can rerun.
Experience
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2021 to 2023
Customer Insights Analyst
KeyGap Software Solutions, Bangalore, India
- Produced customer behaviour and campaign performance reporting for product and marketing managers across two software clients.
- Wrote SQL queries for weekly active users, retention and conversion, and kept the Tableau dashboards used in fortnightly review meetings up to date.
- Built a recurring KPI report covering acquisition, activation, retention and revenue, which replaced a spreadsheet that had been updated by hand.
- Supported A/B test reporting for landing pages and email campaigns, and worked with developers and product owners through sprint planning in JIRA.
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2018 to 2020
Product Back Office Executive
PowerWeave Software Solutions, Mumbai, India
- Managed product listings and catalogue updates for a United States wholesale platform, working with large product datasets in Excel.
- Handled order processing, shipping coordination and quotations, and tidied up the recurring data entry steps to cut errors and turnaround time.
- Acted as a regular point of contact for trade partners on the account.
Education
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2025 to 2027 (expected)
Master of Data Science
Swinburne University of Technology, Melbourne
- Statistical data analysis, machine learning, database systems, big data processing, data visualisation and predictive analytics.
- Coursework in Python (pandas, scikit-learn, matplotlib, seaborn), R, SQL, Tableau and Git.
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2020 to 2022
Master of Business Administration, Marketing and Business Analytics
GIBS Business School, Bangalore City University, India
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2017 to 2020
Bachelor of Commerce, Marketing
Tilak Maharashtra Vidyapeeth, Pune, India
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Certification
Data Visualisation with Tableau Specialisation
University of California, Davis, on Coursera. View the certificate
Tools I use
Everything listed here I have actually used on a project, not just read about.
- Querying
- SQL (MS SQL Server, PostgreSQL, MySQL, joins, CTEs, window functions), data modelling for reporting
- Data preparation
- Cleaning, standardising and combining source data in Python, train, validation and test split design, validation and reconciliation against the source
- Programming
- Python (pandas, NumPy, scikit-learn, matplotlib, seaborn), R
- Engineering
- Git and GitHub, GitHub Actions, Docker, pytest, Django REST, Streamlit
- Reporting
- Tableau, Power BI, Looker Studio, Excel (pivot tables, lookups), KPI and dashboard design
- Analysis
- Statistical analysis, A/B testing, regression, clustering, forecasting, cohort and retention analysis, survey weighting
- Other
- Google Analytics 4, Jupyter, JIRA, stakeholder reporting
- Studying now
- Big data processing, database systems, machine learning, predictive analytics, transformers and NLP