Head of Data Engineering

About Radix

Radix is revolutionizing how the multifamily world makes decisions. From investment to divestment, and everything in between, Radix brings data transparency, market intelligence, and acquisition modeling into a seamless ecosystem that turns the industry's best insights into confident decisions.

Radix is building an agentic first engineering organization where AI agents operate alongside humans as first class actors within our systems, and data is the foundation that powers it all. As Head of Data Engineering, you will define and scale the platform, semantic layer, and AI ready data infrastructure that power trusted analytics, conversational experiences, and next generation AI products across the company.

You will lead a highly technical, hands‑on team responsible for improving the reliability, quality, scalability, and maturity of a complex multi‑product data ecosystem while helping shape the future of AI enabled decision making orting directly to the CTO, you will have deep visibility into product direction, engineering strategy, and business priorities while owning one of the most critical foundations of the company.

• Lead, coach, and grow a high-performing team of data engineers while remaining deeply hands‑on in architecture, development, and operational excellence.
• Own the architecture, scalability, governance, cost optimization, and operational health of the Radix data platform built on Databricks and AWS.
• Design, build, and maintain reliable ETL and ELT pipelines that transform data from Mongo
  
  DB, Postgre
  
  SQL, APIs, and additional source systems into trusted analytics‑ready models.
• Define and enforce data contracts, modeling standards, testing coverage, schema expectations, and quality practices that create trust and consistency across analytics, product, and AI systems.
• Own and evolve the dbt environment including project architecture, semantic definitions, modeling conventions, Metric Flow strategy, documentation standards, and analytics engineering best practices.
• Build and scale the semantic layer that powers Radix AI products, enabling conversational data experiences, natural language query capabilities, and trusted LLM consumption.
• Architect and manage the MCP based data exposure layer that governs how AI agents access, retrieve, and interact with enterprise data safely and reliably.
• Establish production‑grade observability across pipelines and workflows including lineage, monitoring, anomaly detection, alerting, operational runbooks, and incident prevention practices.
• Drive infrastructure as code and CI/CD best practices across the data ecosystem using Terraform, automated testing, deployment gates, rollback strategies, and deployment automation.
• Evaluate emerging AI, semantic layer, and analytics tooling while making principled build‑versus‑buy decisions that support long‑term scalability, developer productivity, and platform maturity.

• 5+ years of experience in data engineering, analytics engineering, or closely related disciplines operating production‑grade data systems at scale.
• Deep expertise with Databricks or comparable modern data platforms including Spark optimization, orchestration, governance, Unity Catalog or equivalent tooling, and cluster management.
• Strong experience with dbt including advanced modeling patterns, semantic layers, Metric Flow, testing strategies, and analytics engineering best practices.
• Hands‑on experience building pipelines from Mongo
  
  DB, Postgre
  
  SQL, and third‑party APIs with a deep understanding of source system behavior, data modeling, and reliability concerns.
• Strong proficiency in Python for pipeline engineering, transformation logic, automation, and developer tooling.
• Experience with modern open table and columnar storage formats including Delta Lake, Apache Iceberg, and Parquet.
• Strong AWS knowledge across services that support modern data infrastructure including S3, Glue, Lambda, IAM, VPC, and compute services.
• Infrastructure as code mindset with Terraform or comparable tooling alongside experience implementing CI/CD, automated testing, deployment workflows, and safe release…