Data Engineer

Radix•Scottsdale, AZ

8d•Hybrid

About The Position

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.

Requirements

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 data infrastructure, including AWS, Terraform, CI/CD, orchestration platforms (Dagster, Airflow, or similar), observability, monitoring, lineage, and data quality frameworks.
Comfortable working in hybrid-cloud environments and making sound decisions around architecture, performance, cost optimization, reliability, and operational readiness.
Able to trace and resolve data issues end-to-end, implement effective data contracts, and identify risks or anti-patterns in modeling, pipeline design, and system operations before they become problems.
Comfortable navigating ambiguity, simplifying complex systems, and improving data quality and engineering practices without slowing delivery.
Actively leverage AI tools and understand how modern development practices, automation, and agentic workflows can improve engineering productivity while maintaining trust, accuracy, and governance.
Familiar with designing data platforms that support analytics, applications, and AI use cases through clean ontologies, predictable schemas, semantic layers, and trustworthy metrics.
Hands-on builder who leads through technical credibility, strong communication, collaboration, and sound judgment rather than authority.
High ownership mindset with a track record of delivering meaningful improvements in fast-moving, evolving environments.
Excited by the opportunity to build reliable, understandable, and scalable data systems that create measurable value for humans, applications, and AI agents.

Responsibilities

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.