Data Engineer - Python

About Merito

Despite the wave of D2C brands in India, most of them are flying blind.

They don't know if their unit economics are healthy. They don't know how they compare to competitors. They don't know if they're creditworthy - and neither do their investors.

Merito is building the financial operating system for India's D2C seller economy. The layer of intelligence that sits between a brand and every financial decision it makes.

800+ brands (with over Rs. 6,000 Cr in sales) use Merito today. 90%+ organically. We did this with just 8 people.

Apart from having marquee angels, we've just closed a Rs. 35 Cr round - with an additional future commitment of Rs. 100 Cr.

Go to clickmerito.com to learn more.

Are you right for this role?

Must-Have Attributes

Minimum 2 years of experience as a data engineer in a tech startup (scaled past Series B or >1mn MAU/1000 B2B consumers)

Python - strong proficiency; primary language for all data work

SQL - complex queries, data extraction and transformation

ClickHouse or any OLAP database, analytical database design and querying

Pandas & NumPy - data cleaning, transformation and processing

Web scraping & RPA - hands on experience with Playwright or Selenium

REST API integrations - consuming and managing third party API integrations

Based in/willing to relocate to Mumbai

Available to start in less than 45 days

Good-to-Have Attributes

Agentic workflows - experience with LangChain, LLM API integration or AI assisted automation

FastAPI - building lightweight internal services and APIs

Experience working with ecommerce or quick commerce data

Degree in Computer Science, Statistics, or a related field with a GPA of 7.0 or higher

Overview of the Job

This is a high-ownership role at the core of Merito’s data infrastructure. You will

• Design, build and maintain Python-based data pipelines that power Merito’s analytics and credit decisioning
• Own and optimise the ClickHouse analytics database, schema design, query performance and data reliability
• Work closely with the product and engineering teams to ensure clean, timely, and trustworthy data
• Build internal data tooling and dashboards to surface insights across the business
• Proactively monitor pipeline health and resolve data quality issues