Data Engineer

Responsibilities:
Architect the entire data platform from scratch, including event capture, batch and streaming pipelines, and feature engineering.
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.
Requirements:
You're a systems thinker who starts with data and designs for scale.
You've likely had exposure at early-stage or high-scale consumer platforms: social, gaming, transactions, or media.
4-8 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.

Experience
4-8 yrs