Data Engineer
Radix is building the most trusted data and analytics platform in multifamily. Joining now means stepping into a place where curiosity wins, ideas move quickly, and your impact shapes an industry.
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.
What You Will Be Part Of
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, modest technical debt, 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.
What You Will Achieve
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.
What Makes You a Strong Fit
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, with Terraform preferred.
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.
Why You Will Love Working Here
Comprehensive Benefits
Medical, dental and vision coverage designed to support your wellbeing.
Uncapped Paid Time Off
We trust you to take the time you need to recharge and do your best work.
Pre-IPO Equity
Meaningful ownership with the potential to grow right alongside Radix.
Performance Bonus
Rewarded for the impact you drive, not just tasks completed.
Learn From the Best
Work shoulder-to-shoulder with industry experts, innovative operators, and leaders shaping the future of multifamily.
Build Category-Defining Products
Join us during a high-velocity, post–Series A growth stage where your ideas influence the market, not just the roadmap.
Radix does not accept unsolicited resumes or candidate submissions from staffing or recruiting agencies. We do not engage external agencies for hiring and will not be responsible for any fees associated with unsolicited submissions.
Please note that Radix does not engage candidates for this position on a Corp to Corp (C2C), contract, or third party basis, and is unable to provide visa sponsorship (including H-1B, TN, OPT/STEM OPT or similar).
About Radix
Our mission is to advance intelligent decision-making across the multifamily ecosystem, because better decisions shape how we live, invest, and grow.