Data Engineer

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.

We’re looking for a highly capable, hands‑on Data Engineer to help build the foundation for Radix’s next generation of data and AI products.

This is a strong individual contributor role for someone who wants to solve hard data problems, build reliable systems, and raise the bar for how data moves, models, and powers decision‑making across the business. You will work closely with the Head of Data Engineering and cross‑functional partners across Product, Engineering, Analytics, and AI Systems to improve the quality, reliability, and usability of Radix’s data platform.

You’ll be stepping into an environment with multi-product system complexity, a real legacy estate that we are actively retiring rather than living with, an in‑flight migration between clouds, and real stakes. The right person will be able to diagnose where trust breaks down, improve pipeline reliability, strengthen data modeling practices, and build production‑grade systems that support both analytics and AI‑enabled product experiences.

Beyond traditional data engineering, this role will help shape the semantic layer that powers Radix’s AI products. That means designing data systems with LLM consumption in mind: clean metric definitions, reliable context surfaces, governed access patterns, and data infrastructure that agents can actually trust.

• Design, build, and maintain scalable ETL/ELT pipelines that ingest data from files, databases, APIs, and third‑party systems into trusted, analytics‑ready data products.
• Own the reliability, performance, governance, and cost optimization of Radix's Databricks‑based data platform, including orchestration, data quality, observability, and root cause analysis.
• Build and maintain robust dbt models, testing frameworks, documentation, schema contracts, and semantic‑layer assets that create trusted, scalable data products.
• Define and enforce data contracts, metric definitions, and modeling standards that support consistent use across analytics, product, and AI applications.
• Partner across teams to design reliable, governed data interfaces and ensure downstream consumers can confidently use and trust the data they depend on.
• Help build the semantic layer and AI data infrastructure that powers conversational analytics, LLM-driven experiences, and agentic workflows.
• Design and maintain metadata, business definitions, evaluation frameworks, and governance guardrails that improve the trustworthiness of AI-generated insights.
• Build and maintain infrastructure‑as‑code, CI/CD pipelines, automated testing, and deployment practices that enable reliable and scalable data platform operations.
• Improve developer experience through better tooling, local development workflows, testing practices, environment consistency, and deployment confidence.
• Implement production‑grade monitoring, lineage, anomaly detection, alerting, and operational processes that proactively identify and resolve data issues.
• Create documentation, runbooks, and operational playbooks that improve platform supportability, knowledge sharing, and long‑term scalability.
• Operate as a highly collaborative technical leader who aligns stakeholders, documents decisions, and helps drive a reliable, scalable, and AI‑ready data ecosystem.

• 5+ years of experience in Data Engineering, Analytics Engineering, or a related field, with hands‑on responsibility for production data platforms and pipelines.
• Deep expertise with Databricks (or similar modern data platforms), dbt, SQL, Python, and scalable data modeling, including governance, testing, documentation, and semantic layers.
• Experience building and maintaining reliable data pipelines from relational databases, document stores, and object storage using modern table formats such as Delta Lake, Iceberg, and Parquet.
• Strong understanding of cloud‑based…