Job
Description
Role Overview Looking for a Data Engineer to design, build, and scale data infrastructure that supports core recommendation systems and personalization engines. As an early team member, you will help establish the data foundation for onboarding flows, engagement metrics, dashboards, and ML model pipelines. You’ll work closely with machine learning and product teams to build reliable data adapters and experimentation frameworks. Key Responsibilities Build and scale ETL pipelines to transform raw app events into ML-ready features Develop real-time and batch data pipelines to process user interactions and system events Contribute to feature stores, embedding pipelines, data validation tools, and freshness monitoring systems Design robust data layers to support A/B testing, metrics tracking, and model training workflows Partner with ML engineers to deliver input adapters, training loops, and embedding generation modules Continuously evolve the core data model and event schema with an emphasis on privacy, observability, and performance What We Look For Skills: 3–6 years of experience building scalable data infrastructure in high-velocity environments Exposure to recommendation systems, ML workflows, or content feeds in B2C platforms (social, media, gaming, etc.) A strong understanding of how data features impact machine learning models and outcomes Hands-on experience with modern data tooling such as Kafka, Spark, Flink, Airflow, dbt, Redis, BigQuery, or Terraform Product-first thinking with an understanding of how data systems directly affect user experience Qualifications Required Experience: 3–6 years working with scalable data systems Bonus Points: Experience in personalization or ranking models, recommender pipelines, A/B testing frameworks, or ML experimentation tools; passion for building consumer-facing products Location: Gurugram What We Offer Competitive compensation and benefits Ownership of foundational data systems with direct impact on product direction Opportunities for professional growth and cross-functional collaboration A fast-paced, collaborative, and experimentation-driven work environment Show more Show less
