Data Engineer

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About Company

An early-stage, US-based venture-backed technology company focused on creating innovative platforms for building meaningful relationships. The company aims to transform how people connect by harnessing artificial intelligence, fostering community engagement, and delivering tailored content. Rather than developing another conventional social app, it is crafting a unique experience that resonates deeply with users, making them feel truly understood. Central to its platform is a dynamic, machine-learning-powered recommendation system, drawing inspiration from the personalised discovery engines of leading music and video platforms. With strong financial backing from top-tier venture capital firms in India and the United States, the company is well-positioned to advance its mission with innovation and impact.

Position Overview

You design and scale the data platform that powers machine-learning recommendations and personalisation across the client’s consumer app. As a founding data engineer, you own pipelines, storage, and tooling that unlock fast experimentation for the ML and product teams. You collaborate directly with ML engineers and leadership to shape data strategy from day zero.

Role & Responsibilities

• You build and scale ETL pipelines that convert raw app events into ML-ready features.
• You design real-time and batch pipelines for user actions and events.
• You contribute to the feature store, embedding pipelines, data validation, and freshness monitoring systems.
• You design data layers used in A/B tests, product metrics, and ML training.
• You partner with ML engineers to deliver data adapters for model inputs, embedding generation, and training loops.
• You evolve the core event schema and data model with privacy, observability, and performance in mind.

Must have Criteria

• 3–6 years of experience building scalable data systems in fast-moving environments.
• Background supporting recommendation systems, machine learning, or content feeds in social or consumer platforms.
• Understanding of how features feed into ML models and experience working closely with ML teams.
• Product empathy and awareness of how data impacts user experience beyond analytics.
• Proficiency with tools such as Kafka, Spark, Flink, Airflow, dbt, Redis, BigQuery, Feast, and Terraform, with the ability to choose the right tool for the job.

Nice to Have

• Experience with personalisation systems, recommender pipelines, or ranking models.
• Exposure to A/B testing platforms or online experimentation infrastructure.
• Passion for consumer social or dating products.

What We Offer

• You join a founding data team where your work is core to the product experience.
• You shape key decisions for a platform that directly impacts how people form meaningful relationships.
• Significant ESOPs and wealth creation alongside market-competitive cash compensation.