Head of Data Engineering Job at Radix (AZ) (Scottsdale)

Job Description

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.

Job Responsibility

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 MongoDB, PostgreSQL, 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, MetricFlow 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

Requirements

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, MetricFlow, testing strategies, and analytics engineering best practices
Hands on experience building pipelines from MongoDB, PostgreSQL, 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 practices for data platforms
Familiarity with orchestration, observability, and data quality tooling including Airflow, Dagster, Great Expectations, Soda, Monte Carlo, or similar platforms
Understanding of both batch and streaming data patterns including awareness of technologies such as Kafka and Kinesis
Strong systems thinking with the ability to design stable, scalable data interfaces and trace issues from ingestion through downstream analytics, applications, and AI consumers
You actively use AI tooling in your own engineering workflows and understand how to design data systems, semantic layers, and governed context surfaces optimized for LLM consumption
You lead through technical credibility, transparency, and sound judgment while remaining deeply hands on alongside your team, thrive in startup environments that require curiosity, resilience, and adaptability, have experience improving complex systems and raising engineering standards without slowing delivery momentum, and bring a builder mindset focused on solving problems and driving visible impact across the business

What we offer

Medical, dental and vision coverage designed to support your wellbeing
Uncapped Paid Time Off
Pre-IPO Equity
Performance Bonus
Learn From the Best
Build Category-Defining Products

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Head of Data Engineering

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