Senior Product Manager - Platforms

About Snapmint:

India's booming consumer market has over 300 million credit-eligible consumers, yet only 35 million actively use credit cards. At Snapmint, we are building a better alternative to credit card that lets consumers buy now and pay later for a wide variety of products, be it shoes, clothes, fashion accessories, clothes or mobile phones. We firmly believe that an enduring financial services business must be built on the bedrock of providing honest, transparent and fair terms.

Founded in 2017, today we are the leading online zero-cost EMI provider in India. We have served over 10M consumers across 2,200 cities and are doubling year on year. Our founders are serial entrepreneurs and alumni of IIT Bombay and ISB with over two decades of experience across leading organizations like Swiggy, Oyo, Maruti Suzuki and ZS Associates before successfully scaling and exiting businesses in patent analytics, ad-tech and bank-tech software services.

Role Overview:

We're looking for a Senior Product Manager – Platforms to build and own the internal platform capabilities that power how Snapmint's product team's work. This is a highly technical, cross-cutting role that spans three areas:

1. Product analytics and data platforms such as Mixpanel

2. A/B experimentation infrastructure and tooling such as Unleash, and

3. Engineering productivity — working with tech teams to reduce bug rates and accelerate feature delivery.

You will be a force multiplier for every product team at Snapmint by ensuring they have the data, experimentation, and AI-assisted analytics tools needed to make faster, more confident decisions.

Key Responsibilities:

Data Analytics Platform

• Own the product analytics stack (e.g. Mixpanel) — drive adoption, governance, and capability

expansion across product and growth teams

• Work with data engineers to define event taxonomies, ensure instrumentation quality, and

maintain data pipelines that power product experiments

• Enable AI-assisted analytics capabilities (e.g. anomaly detection, automated insights) for

product managers and the data analytics team

• Partner with data analytics to build self-serve dashboards and reporting frameworks that

reduce dependency on ad-hoc data pulls

A/B Experimentation Platform

• Build and maintain the A/B experimentation platform (e.g. Unleash or equivalent) to enable

product teams to run rigorous, statistically sound experiments

• Define best practices, guardrails, and documentation for experiment design, feature flagging,

and rollout management

• Ensure experiment data quality and work closely with data engineering to validate results and

prevent instrumentation errors

• Drive a culture of experimentation across product teams by making it easy to set up, monitor,

and conclude experiments

Engineering Productivity & Delivery

• Partner with engineering leads to identify and reduce sources of bugs, technical debt, and

delivery bottlenecks across the product org

• Define and track engineering health metrics such as bug escape rate, cycle time, deployment

frequency, and MTTR

• Champion processes and tooling that improve feature delivery speed — from sprint planning

to release management

• Act as a connective layer between product and engineering to ensure platform investments

translate into measurable improvements in shipping velocity

Requirements:

• 5–7 years of product management experience, with at least 2–3 years in a platform, infrastructure, or internal tooling product role

• Prior experience at a B2C company with significant scale; experience at a top consumer internet or fintech company is a strong plus

• Hands-on familiarity with analytics tools (e.g. Mixpanel, Amplitude, Segment) and experimentation platforms (e.g. Unleash, LaunchDarkly, Optimizely)

• Technical depth to engage credibly with data engineers and software engineers — ability to

understand data pipelines, event schemas, feature flags, and API integrations

• Strong understanding of data quality, experimentation methodology, and statistical significance

• Experience working with AI/ML-powered analytics tools or enabling data teams to adopt them

is a plus

• Excellent stakeholder management skills — able to influence without authority across product, engineering, and data teams

• Data-driven and highly structured in prioritizing platform investments against business impact