Head of Engineering

Role Summary

About Our Client: Our client is a frontier AI company building a next-generation AI platform - a generative AI + simulation-powered search engine.

The Role: Lead end-to-end delivery of data engineering initiatives. Architect and scale the core data infrastructure that powers their business - from data lakes and enterprise data platforms to AI-enabled analytics products and agentic systems. High-impact opportunity to build foundational systems that drive decision-making across research, product, and commercial teams.

Responsibilities

• Data Infrastructure & Architecture
• Design, build, and scale data pipelines and lakehouse architectures supporting enterprise, product, and commercial analytics at scale
• Own the data lake ecosystem, defining standards for ingestion, storage, transformation, and access across structured and unstructured data
• Evolve the data stack for scalability, performance, and developer experience, optimizing for multi-cloud compute and supercomputing environments

Build and maintain centralized feature registry / feature store as single source of truth for feature cataloging, lineage, ownership, SLAs - ensuring training/serving consistency

Data Products & Platforms

Develop and own core data products including enterprise data platform, intelligence layer, and AI-powered analytics tools (including AI agents) for non-technical users

• Build robust data models supporting analytics, reporting, and ML across multiple business lines
• Enable Applied AI/ML Engineers (Agents) building agents that automate workflows
• Governance & Data Quality
• Champion data quality, governance, observability to ensure organization-wide trust in data
• Implement lineage and auditability for training data used in generative models

Requirements

• 12-15+ years designing and building data products - enterprise data platforms, analytics platforms, personalization systems in AI-native environments
• Hands-on lakehouse ecosystems, low-latency large-scale batch and streaming pipelines for ML optimized for GPU compute
• Feature stores, Spark/Ray/Dask, Databricks/Snowflake/Delta Lake/Iceberg
• Deep SQL, Spark, Python. Governance & observability tooling
• Translates complex technical concepts for product and commercial teams
• Scrappy startup experience - as technical as possible, as commercial as possible