Data Analyst

We are looking for a data-driven analyst who can decode what's happening across our marketing channels, e-commerce platforms, social media, and quick commerce — and turn that into clear business insights. You will also own data extraction pipelines — scraping any platform required to feed clean, structured inputs into our AI intelligence platform.

Key Responsibilities

Data Extraction and Pipeline Management

• Extract data from any platform required as input for the AI intelligence model — including e-commerce sites, social media platforms, quick commerce apps, review aggregators, marketplaces, beauty forums, and any other relevant source

• Choose the appropriate extraction method per platform — web scraping, API integration, or platform data exports — based on what is available and permitted

• Deliver all extracted data in a clean, structured format as per specifications defined by the AI Engineer

• Maintain all pipelines on a scheduled basis and proactively monitor for failures, platform structure changes, or data quality issues

• Document all active pipelines including source, extraction method, update frequency, and known limitations

Data Analysis and Business Insights:

• Analyze performance data across all key channels and platforms including digital marketing (Google Ads, Meta), e-commerce (Nykaa, Amazon, Purplle), quick commerce (Blinkit, Zepto, Swiggy Instamart), and social media (Instagram, YouTube)

• Track and report on critical metrics per channel — ROAS, CAC, CTR, conversion rate, revenue attribution, rankings, sell-through rates, engagement, and reach

• Identify trends, patterns, and anomalies across platforms and translate them into clear, actionable business recommendations for marketing, R&D, and other departments

• Benchmark brand performance against key competitors across all channels and surface gaps and opportunities

• Monitor consumer sentiment, trending ingredients, and category conversations through reviews, comments, and UGC

What We're Looking For

• 0–2 years of work experience in a data, analytics, or reporting role — internships and academic projects count

• Proficient Excel skills including functions like VLOOKUP, HLOOKUP, INDEX-MATCH, FILTER, SUMIF, PIVOT TABLES, and other advanced functions used in day-to-day data analysis. Ability to extract, structure, and analyze large datasets in Excel without relying on external tools is essential.

• Comfortable with pulling data from BigQuery — primarily customer engagement and behavioural data — and should understand how to write or interpret basic queries to get the data they need. Understanding of sales data structures — how to read, extract, and interpret sell-through rates, return rates, revenue trends, and product-level performance — is equally important.

• Candidate should have a strong understanding of the key parameters and metrics that matter on e-commerce and quick commerce platforms — such as visibility scores, keyword rankings, conversion rates, sell-through rates, return rates, ratings and review trends, and promotional performance. They should be able to independently navigate, extract, and derive meaningful insights from platform reports across Nykaa, Amazon, Purplle, Blinkit, Zepto, and Swiggy Instamart without hand-holding. In addition, data from Meta Ads Manager and Google Ads should also be factored into the overall analysis to give a complete picture of brand performance.

• For data extraction and pipeline work, strong Python skills are essential — specifically libraries like BeautifulSoup, Scrapy, Playwright, or Selenium along with ability to work with REST APIs and platform data exports.

Good to Have

• Basic data manipulation and visualisation

• SQL basics for querying structured datasets and knowledge of scheduling tools like cron jobs for pipeline automation would be a strong addition

• Knowledge of financial reporting — P&L, budget vs actuals, working capital

• Any recognised certification — Google Data Analytics, Microsoft Power BI, SQL for Data Science

• Experience working with large datasets — 100K+ rows — using structured tools

Metrics You'll Work With

Revenue, Orders, AOV, Conversion Rate, Returns, CAC,Channel Mix ,Fill Rate, OTIF, Inventory Turnover, Supplier Lead Time

Why This Role

• Real ownership from day one — not shadow projects or support tasks

• Cross-functional exposure — you'll understand how sales, finance, marketing, and ops connect

• Fast growth — analysts here solve real problems, not just build weekly reports

• Your work shapes decisions — you'll be in the room, not just sending decks

• Structured mentorship and a clear path to Senior Analyst or Business Analyst roles

We are an equal opportunity employer. Every application is reviewed on merit alone.

0–2 years of work experience in a data, analytics, or reporting role — internships and academic projects count

• Proficient Excel skills including functions like VLOOKUP, HLOOKUP, INDEX-MATCH, FILTER, SUMIF, PIVOT TABLES, and other advanced functions used in day-to-day data analysis. Ability to extract, structure, and analyze large datasets in Excel without relying on external tools is essential.

• Comfortable with pulling data from BigQuery — primarily customer engagement and behavioural data — and should understand how to write or interpret basic queries to get the data they need. Understanding of sales data structures — how to read, extract, and interpret sell-through rates, return rates, revenue trends, and product-level performance — is equally important.

• Candidate should have a strong understanding of the key parameters and metrics that matter on e-commerce and quick commerce platforms — such as visibility scores, keyword rankings, conversion rates, sell-through rates, return rates, ratings and review trends, and promotional performance. They should be able to independently navigate, extract, and derive meaningful insights from platform reports across Nykaa, Amazon, Purplle, Blinkit, Zepto, and Swiggy Instamart without hand-holding. In addition, data from Meta Ads Manager and Google Ads should also be factored into the overall analysis to give a complete picture of brand performance.

• For data extraction and pipeline work, strong Python skills are essential — specifically libraries like BeautifulSoup, Scrapy, Playwright, or Selenium along with ability to work with REST APIs and platform data exports.