Data Engineer

Radix is hiring a hands-on, individual contributor Data Engineer to build reliable data and AI foundations. This onsite role in Scottsdale, AZ focuses on ETL/ELT pipelines, a Databricks-supported warehouse, dbt data modeling, and a governed semantic layer designed for LLM consumption. You will partner with data engineering leadership and the Infrastructure and AI Systems team to help ensure analytics, product, and AI experiences run on trusted data.

Why this role

• Build the data foundation for BI, product, and AI, with a governed semantic layer that keeps metrics consistent across surfaces.
• Own reliability end-to-end, from pipeline ingestion through data quality monitoring, lineage, and operational runbooks.
• Work in a high-velocity post-Series A environment where your decisions influence what gets built next.
• Collaborate shoulder-to-shoulder with industry experts and leaders shaping the future of multifamily.

Responsibilities

• Build and maintain reliable ETL/ELT pipelines moving data from MongoDB, PostgreSQL, and third-party APIs into clean, analytics-ready models.
• Support the Databricks data warehouse, including cluster configuration, job orchestration, cost optimization, and governance.
• Create data contracts so downstream consumers can trust the data products they use.
• Own data quality across pipelines and models, including detection, alerting, root cause analysis, and resolution.
• Develop and maintain tested, well-documented dbt models that analytics consumers can understand and use confidently.
• Establish pragmatic dbt conventions that keep the model clear, consistent, and scalable.
• Implement dbt-native tests, documentation, schema contracts, source freshness checks, and model expectations for critical assets.
• Treat the data model as a product, emphasizing clarity, trust, and usability.
• Help build a governed, LLM-consumable semantic layer with consistent metrics, dimensions, and measures across BI, product, and AI.
• Design models, metrics, and entity definitions that support conversational SQL and natural language queries.
• Contribute to the Model Context Protocol data exposure layer, including what surfaces are exposed, how they are accessed, and what guardrails apply.
• Partner with the Head of Data Engineering and the Infrastructure and AI Systems team on data context and retrieval surfaces used by agents.
• Evaluate semantic and AI data tools such as Cube, dbt Semantic Layer, MetricFlow, and LookML-style metric registries.
• Build and maintain infrastructure as code for Databricks, pipeline compute, and supporting AWS services, with Terraform as the default.
• Improve CI/CD for data pipelines, including automated testing, deployment gates, rollback mechanisms, and safer releases.
• Instrument workflows with production-grade observability, including metrics, alerting, lineage, anomaly detection, and data quality monitoring.
• Improve developer experience with local development, testing patterns, environment parity, and higher deployment confidence.
• Create documentation, runbooks, and operational playbooks to help the platform scale and stay supportable.

Requirements

• 5+ years of experience in data engineering, analytics engineering, or a closely related field, including operating data systems in production.
• Deep fluency with Databricks or a comparable data warehouse, including cluster management, Spark tuning, job orchestration, and Unity Catalog (or equivalent governance).
• Strong dbt expertise, including project architecture, advanced modeling patterns, testing, documentation, and semantic layer or MetricFlow concepts.
• Hands-on experience with MongoDB and PostgreSQL as pipeline sources, including data models, change patterns, and failure modes.
• Proficiency with columnar and open table formats such as Parquet, Delta Lake, and/or Apache Iceberg.
• Strong Python skills for pipeline development, transformation logic, and automation.
• AWS fluency across S3, Glue, Lambda, IAM, VPC, and supporting compute.
• Experience with infrastructure as code, with Terraform preferred.
• Experience building CI/CD practices for data pipelines, including testing and safe release workflows.
• Hands-on experience with data observability, including pipeline monitoring, data quality alerting, and lineage (for example: Great Expectations, Soda, Monte Carlo, or equivalent).
• Familiarity with orchestration frameworks beyond dbt, such as Airflow or Dagster (or equivalent).
• Awareness of streaming patterns such as Kafka or Kinesis, even if primary experience is batch.
• Ability to trace data issues from source systems to downstream BI, product, or AI consumers and identify where trust breaks down.
• Focus on data contracts and interface stability before shipping models others depend on.
• Skill at spotting anti-patterns early across modeling, pipeline design, reliability, and development workflows.
• Commitment to operational readiness, with observability, testing, and supportability treated as part of the definition of done.
• Comfort working through ambiguity and turning messy systems into reliable, understandable ones.
• Active use of AI tools in your development workflow, with curiosity about improving engineering quality and speed.
• Understanding of designing data systems for LLM consumption, including clean ontologies, predictable schemas, well-scoped context surfaces, and trustworthy metric definitions.
• Informed opinions on how semantic layers should support natural language querying and where natural language to SQL breaks down.
• Interest in AI data infrastructure, with the ability to separate practical value from hype.
• Excitement about building data systems that serve humans, applications, and AI agents.
• Hands-on, builder-oriented approach to fix broken or missing components.
• Experience in complex environments with inconsistent standards, technical debt, or low-trust data, improving quality without stopping delivery.
• Leadership through technical credibility, clear communication, and strong judgment rather than authority.
• High ownership and transparent communication, with success in small teams where impact is visible.
• Care for building systems that are reliable, understandable, and useful.

Benefits

• Medical, dental, and vision coverage designed to support your wellbeing.
• Uncapped Paid Time Off.
• Pre-IPO Equity.
• Performance Bonus.
• Work shoulder-to-shoulder with industry experts, innovative operators, and leaders shaping the future of multifamily.
• Join during a high-velocity, post-Series A growth stage where your ideas influence the market, not just the roadmap.

Technologies: MongoDB, PostgreSQL, Databricks, Spark, Unity Catalog, dbt, MetricFlow, Cube, LookML, Python, Parquet, Delta Lake, Apache Iceberg, AWS (S3, Glue, Lambda, IAM, VPC), Terraform, CI/CD, Great Expectations, Soda, Monte Carlo, Airflow, Dagster, Kafka, Kinesis, Model Context Protocol, LLM.