Data Analyst

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

•     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.

•     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

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

•     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

Benefits

     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.