About the role
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 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.
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.