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.