Data Engineer

Radix is building data and AI products for the multifamily industry. The Data Engineer will build and improve reliable data pipelines, models, and platform infrastructure, and help develop a governed semantic layer that supports analytics and AI experiences.

Responsibilities

• Build and maintain reliable ETL/ELT pipelines that move 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
• Build data contracts so downstream consumers can trust the data products they use
• Own data quality in your pipelines and models, from detection and alerting to root cause analysis and resolution
• Build and maintain reliable, tested, well-documented dbt models that are easy for analytics consumers to understand
• Help establish pragmatic dbt conventions that keep the data model clear, consistent, and scalable
• Implement dbt-native tests, documentation, schema contracts, source freshness checks, and model expectations for critical data assets
• Treat the data model as a product, with a focus on clarity, trust, and usability
• Help build the governed, LLM-consumable semantic layer that powers Radix’s AI products, with consistent metrics, dimensions, and measures used across BI, product, and AI surfaces
• Design models, metrics, and entity definitions that support conversational SQL and natural language queries
• Contribute to the Model Context Protocol data exposure layer, including which data surfaces are exposed, how they are accessed, and what guardrails are needed
• Partner with the Head of Data Engineering and the Infrastructure and AI Systems team on the data context and retrieval surfaces agents rely on
• 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 data workflows with production-grade observability, including metrics, alerting, lineage, anomaly detection, and data quality monitoring, so issues surface before they reach the business or customers
• Improve the developer experience, including local development, testing patterns, environment parity, and deployment confidence
• Create documentation, runbooks, and operational playbooks that make the platform easier to understand, support, and scale

Skills

• 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 their 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, automation, and tooling
• AWS fluency across data infrastructure services, including S3, Glue, Lambda, IAM, VPC, and supporting compute
• Experience with infrastructure as code
• Experience building CI/CD practices for data pipelines, including testing, deployment gates, and safe release workflows
• Hands-on experience with data observability, including pipeline monitoring, data quality alerting, and lineage, using tools such as Great Expectations, Soda, Monte Carlo, or equivalent
• Familiarity with orchestration frameworks beyond dbt, such as Airflow, Dagster, or equivalent
• Awareness of streaming patterns such as Kafka or Kinesis, even if your primary experience is batch
• Ability to trace a data issue from source system to downstream BI, product, or AI consumer 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 in modeling, pipeline design, reliability, and development workflows
• Commitment to operational readiness, with observability, testing, and supportability part of your definition of done
• Comfort working through ambiguity and turning messy systems into reliable, understandable ones
• Active use of AI tools in your own development workflow, with curiosity about how they improve engineering quality and speed
• Understanding of how to design 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 the AI data infrastructure space, 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: when something is broken or missing, you help fix it
• Experience in complex environments with inconsistent standards, technical debt, or low-trust data, and a track record of improving quality without stopping delivery
• Leadership through technical credibility, clear communication, and strong judgment rather than authority
• High ownership and transparent communication
• Success in small teams where your decisions have direct impact and your contributions are visible
• Care for building systems that are reliable, understandable, and useful to the people and products that depend on them
• Unable to provide visa sponsorship (including H-1B, TN, OPT/STEM OPT or similar)
• Terraform preferred

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 us during a high-velocity, post–Series A growth stage where your ideas influence the market, not just the roadmap.

Company Overview