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3 Days ago

Data Engineer

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Panchkula, Haryana, India
Information Technology
Full-Time
Meant To Be Inc

Overview

We are looking for a Data Engineer to help design, build, and scale the data platform and infrastructure that powers our core recommendation systems and personalisation engines.

You’ll be a founding data engineer helping us lay the pipelines and infrastructure that feed our recommendation systems, onboarding flows, engagement metrics, dashboards, and ML models.

You'll work hand-in-hand with our ML team to build data adapters and interfaces for model training, serving, and experimentation.

Our Philosophy

We believe:

  • Great data + Good models = Great recommendations
  • Good data + Great models = Average recommendations

That’s why we’re investing in data infrastructure from our inception and foundation.

What You'll Do

  • Build and scale ETL from raw app events to ML-ready features
  • Build real-time and batch pipelines for user actions and events
  • Contribute to the feature store, embedding pipelines, data validation, and freshness monitoring systems
  • Design data layers for use in A/B tests, product metrics, and ML training
  • Partner with ML engineers to deliver data adapters for model inputs, embedding generation, and training loops
  • Help evolve the core event schema and data model with privacy, observability, and performance in mind

Ideal Profile

  • You’re a systems thinker who starts with data and designs for scale. You’ve likely exposure at early-stage or high-scale consumer platforms — social, gaming, transactions, or media.
  • 3–6 years of building scalable data systems in fast-moving environments
  • Experience supporting RecSys, ML, or content feeds in social or consumer platforms
  • Understands how features feed into models, or has worked closely with ML teams before
  • Understands how data impacts user experience, not just analytics
  • Kafka, Spark, Flink, Airflow, dbt, Redis, BigQuery, Feast, Terraform, Pick your best tool for the job

Nice to Haves

  • 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
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