{"id":4059362,"url":"https://alion.io/job/radix-data-engineer","title":"Data Engineer","company":{"id":3783751,"name":"Radix","domain":"radix.com","url":"https://alion.io/company/radix-5","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Scottsdale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Great Expectations","optional":false},{"name":"IAM","optional":false},{"name":"LLM","optional":false},{"name":"LookML","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-11T04:22:01Z","employer_posted_date":null,"last_verified_at":"2026-11-11T04:22:01Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"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.\n\nResponsibilities\nBuild and maintain reliable ETL/ELT pipelines that move data from MongoDB, PostgreSQL, and third-party APIs into clean, analytics-ready models\nSupport the Databricks data warehouse, including cluster configuration, job orchestration, cost optimization, and governance\nBuild data contracts so downstream consumers can trust the data products they use\nOwn data quality in your pipelines and models, from detection and alerting to root cause analysis and resolution\nBuild and maintain reliable, tested, well-documented dbt models that are easy for analytics consumers to understand\nHelp establish pragmatic dbt conventions that keep the data model clear, consistent, and scalable\nImplement dbt-native tests, documentation, schema contracts, source freshness checks, and model expectations for critical data assets\nTreat the data model as a product, with a focus on clarity, trust, and usability\nHelp 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\nDesign models, metrics, and entity definitions that support conversational SQL and natural language queries\nContribute to the Model Context Protocol data exposure layer, including which data surfaces are exposed, how they are accessed, and what guardrails are needed\nPartner with the Head of Data Engineering and the Infrastructure and AI Systems team on the data context and retrieval surfaces agents rely on\nEvaluate semantic and AI data tools such as Cube, dbt Semantic Layer, MetricFlow, and LookML-style metric registries\nBuild and maintain infrastructure as code for Databricks, pipeline compute, and supporting AWS services, with Terraform as the default\nImprove CI/CD for data pipelines, including automated testing, deployment gates, rollback mechanisms, and safer releases\nInstrument 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\nImprove the developer experience, including local development, testing patterns, environment parity, and deployment confidence\nCreate documentation, runbooks, and operational playbooks that make the platform easier to understand, support, and scale\n\nSkills\n5+ years of experience in data engineering, analytics engineering, or a closely related field, including operating data systems in production\nDeep fluency with Databricks or a comparable data warehouse, including cluster management, Spark tuning, job orchestration, and Unity Catalog or equivalent governance\nStrong dbt expertise, including project architecture, advanced modeling patterns, testing, documentation, and semantic layer or MetricFlow concepts\nHands-on experience with MongoDB and PostgreSQL as pipeline sources, including their data models, change patterns, and failure modes\nProficiency with columnar and open table formats such as Parquet, Delta Lake, and/or Apache Iceberg\nStrong Python skills for pipeline development, transformation logic, automation, and tooling\nAWS fluency across data infrastructure services, including S3, Glue, Lambda, IAM, VPC, and supporting compute\nExperience with infrastructure as code\nExperience building CI/CD practices for data pipelines, including testing, deployment gates, and safe release workflows\nHands-on experience with data observability, including pipeline monitoring, data quality alerting, and lineage, using tools such as Great Expectations, Soda, Monte Carlo, or equivalent\nFamiliarity with orchestration frameworks beyond dbt, such as Airflow, Dagster, or equivalent\nAwareness of streaming patterns such as Kafka or Kinesis, even if your primary experience is batch\nAbility to trace a data issue from source system to downstream BI, product, or AI consumer and identify where trust breaks down\nFocus on data contracts and interface stability before shipping models others depend on\nSkill at spotting anti-patterns early in modeling, pipeline design, reliability, and development workflows\nCommitment to operational readiness, with observability, testing, and supportability part of your definition of done\nComfort working through ambiguity and turning messy systems into reliable, understandable ones\nActive use of AI tools in your own development workflow, with curiosity about how they improve engineering quality and speed\nUnderstanding of how to design data systems for LLM consumption, including clean ontologies, predictable schemas, well-scoped context surfaces, and trustworthy metric definitions\nInformed opinions on how semantic layers should support natural language querying and where natural language to SQL breaks down\nInterest in the AI data infrastructure space, with the ability to separate practical value from hype\nExcitement about building data systems that serve humans, applications, and AI agents\nHands-on, builder-oriented approach: when something is broken or missing, you help fix it\nExperience in complex environments with inconsistent standards, technical debt, or low-trust data, and a track record of improving quality without stopping delivery\nLeadership through technical credibility, clear communication, and strong judgment rather than authority\nHigh ownership and transparent communication\nSuccess in small teams where your decisions have direct impact and your contributions are visible\nCare for building systems that are reliable, understandable, and useful to the people and products that depend on them\nUnable to provide visa sponsorship (including H-1B, TN, OPT/STEM OPT or similar)\nTerraform preferred\n\nBenefits\nMedical, dental and vision coverage designed to support your wellbeing.\nUncapped paid time off.\nPre-IPO equity.\nPerformance bonus.\nWork shoulder-to-shoulder with industry experts, innovative operators, and leaders shaping the future of multifamily.\nJoin us during a high-velocity, post–Series A growth stage where your ideas influence the market, not just the roadmap.\n\nCompany Overview\nRadix provides a SaaS real-time multifamily data platform helping users make sense of obscure data and drive performance. It was founded in 2015, and is headquartered in Scottsdale, Arizona, USA, with a workforce of 51-200 employees. Its website is https://www.radix.com.","description_format":"text","description_chars":6574,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-11-11T07:18:15Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":25,"reasons":["seen:0","win:early"],"computed_at":"2026-11-11T08:22:10Z"},"pay":null,"html_url":"https://alion.io/job/radix-data-engineer","json_url":"https://alion.io/job/radix-data-engineer.json","meta":{"generated_at":"2026-11-11T08:22:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":13420,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-12T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3783751},"rest":"https://alion.io/mcp/rest/get_company?id=3783751"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fradix-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fradix-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fradix-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/radix-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fradix-data-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4059362}},{"tool":"search_jobs","arguments":{"company":"radix.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4059362},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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