Data Engineer — Recommendation Engine

About the Company

~~ is India's first Gaming Commerce company, pioneering a recent way for 500M+ gamers—an audience growing at 19.6% YoY—to shop inside games.

It is a white-label SaaS plugin that integrates into casual and hyper-casual games, transforming in-game coins into real-world value. Players can redeem them for brand coupons, digital services, or even physical products inside fully customizable in-game stores.

For gaming studios, ~~ directly solves the industry's toughest challenge—low retention—while also boosting monetisation. For brands, it unlocks a massive high-attention audience that already spends billions on in-game experiences, enabling targeted engagement with measurable ROI. Unlike traditional ads, PlaySuper embeds commerce into gameplay itself, improving recall, conversion, and loyalty.

The Role

We're building a recommendation engine that surfaces the right products to the right player at the right moment inside our in-game store. A core challenge: we have limited in-app behavioural data today, so the system must rely heavily on external market signals to make great recommendations from day one.

As our first Data Engineer, you will own the data infrastructure that makes this possible. You'll build the pipelines that collect, process, and serve these signals into our real-time ranking system.

This is a foundational hire. The entire recommendation engine—from Deal Quality Scores to seasonal trends—depends on the pipelines you build.

What You'll Do

- Own external data pipelines—scrapers for Flipkart/Amazon bestseller rankings, PriceHunt/Smartprix for price benchmarking, Google Trends API for brand and category demand signals
- Build the Deal Quality Score pipeline—a daily batch job that computes a competitiveness score for every product in our catalogue, stored in Redis for sub-millisecond lookup at serving time




- Maintain a seasonal and festive calendar—structured data store for trend overlays (IPL, Diwali, back-to-school, etc.)
- Design and own the in-store event schema in ClickHouse that will power behavioural cohorts as in-app data accumulates
- Build ETL infrastructure (S3 + Spark/Glue) for longer-horizon trend and market data
- Own data quality and freshness SLAs—you are responsible when a signal the reco engine depends on breaks silently

What We're Looking For

- 3–5 years of data engineering experience, ideally at a startup or product company
- Strong Python—you write clean, production-grade pipeline code, not just notebooks
- Experience building and maintaining web scrapers or data collection pipelines at scale
- Hands-on experience with a workflow orchestrator—Airflow, Prefect, or equivalent
- Solid SQL; experience with ClickHouse or another OLAP database is a strong plus
- Familiarity with Redis as a serving layer—you understand TTL, key design, and cache invalidation
- Comfortable with AWS—S3, Glue, ECS; you can set up infra without needing DevOps help
- You care about data quality—you monitor pipelines, set up alerts, and feel responsible when something breaks

Strong Plus (Nice to Have)

- Experience with e-commerce or marketplace data—price intelligence, product catalogues, category taxonomy
- Familiarity with recommender system data patterns
- Experience with Spark or distributed processing for larger datasets
- Prior work on gaming or consumer mobile products

Location: Gurgaon

Compensation: Competitive salary with an opportunity to get meaningful equity

Reports to: CTO

Why PlaySuper?

- Your pipelines feed the system that directly drives real GMV for our studio partners
- Small team, fast decisions, no bureaucracy
- Work directly with the CTO on architecture and direction
- Direct impact visible within weeks of shipping

Skills:- Apache Airflow, Python and Apache Kafka

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