Senior Software Engineer

About Ruxlo LinkedIn is your professional network. Facebook and Instagram are your social networks. Ruxlo is your Home's Network. Your Home. Your Neighborhood. Your Town. We're building a profile page for every one of America's 150M+ properties — and the people tied to each one. Most of what happens to a house is forgotten, scattered across a county filing, a shoebox of receipts, and the memory of whoever owned the place three families ago. We're replacing that with one durable record attached to the parcel itself: work recorded by the contractor who did it, confirmed by the owner who paid for it, and it stays with the house when it sells. Think CARFAX for homes. On a public-record foundation, we're building crowdsourced layers competitors can't license — verified contractor work history, deal documents, and real neighborhood connections. Every layer feeds a Property Graph that gets richer, ranks higher in search and AI answer engines, and pulls in the next user for free. One AI-native app — map-based web and mobile, AI-assisted search, freemium — serves homeowners, contractors, investors, builders, and agents. We're early-stage, backed by an experienced SaaS founder — a unique opportunity to help define a category at the intersection of AI, data, and the largest asset class in the country. The Role Our product is only as good as the data flowing through it. We already have a working data platform — an orchestrated pipeline with a layered data model and several source ingestions live in production. You'll own it. As sources multiply and the product's appetite for data grows, someone has to own how raw, messy inputs become the clean, modeled, trustworthy data the whole company builds on. That's this role. This is a data engineer who thinks like an architect — someone who lives in data modeling and isn't satisfied until the warehouse is designed right. You know when a medallion architecture (bronze > silver > gold) earns its keep and how to run each layer well. You can normalize a transactional model to 3NF when integrity matters, and denormalize into clean star schemas when analytics and speed matter — and you know which to reach for and why. You think in data lakehouse terms — open table formats, schema evolution, time travel, the split between storage and compute — not just "a database with some tables." This is a senior IC role building on real foundations, not from zero. There's a monorepo, an orchestration layer, and a medallion pipeline already feeding production. But the modeling standards, the layer contracts, the lakehouse conventions, and the data-quality bar are still yours to set. Integrations and other engineers land raw data at the front door; you turn it into the modeled, documented, reliable product the rest of engineering, analytics, and the app consume. The patterns you set early become how Ruxlo does data as it scales. You're hands-on and pragmatic. You'd rather ship a well-modeled, well-tested pipeline than write an architecture deck about one. You care about correctness, lineage, and cost — and you use AI-assisted tooling so your time goes to modeling and data quality, not boilerplate. What You'll Do Own the data model. Design and evolve the layered model end to end — raw landing through bronze, silver, and gold — so data is progressively cleaned, conformed, and made trustworthy. Decide where 3NF integrity belongs and where star-schema denormalization serves analytics better. Run the lakehouse. Own our lakehouse architecture — open table formats, partitioning, schema evolution, and the storage/compute split — and make the calls that keep it fast, correct, and cost-efficient as data volume grows. Model for the product and for analytics. Build the dimensional models, conformed dimensions, and marts that power the app's data-driven features and internal analytics, with clear grain, keys, and slowly-changing-dimension handling. Transform reliably. Own transformation as code — modular, tested, version-controlled models with lineage — so changes are safe, reviewable, and observable rather than one-off SQL nobody can trace. Guard data quality. Define and enforce the quality bar: tests, contracts, freshness and volume checks, anomaly detection, and reconciliation — so bad data is caught at the layer boundary, not in production. Serve the data. Make modeled data easy and safe to consume — well-documented tables, stable contracts, and the right serving patterns for the app, analytics, and downstream services. Feed the medallion, from the front door in. Partner with integrations and ingestion so new sources land cleanly and flow through the layers into the gold models — you own everything from raw landing to served output. Set the standard. Establish the modeling conventions, naming, layer contracts, and lakehouse patterns the rest of engineering adopts, and make the build-vs-buy calls on the data stack. Position Requirements Data Modeling & Architecture (the core of this role) Deep, hands-on command of data modeling — you can design a normalized 3NF transactional model and a denormalized star schema for analytics, and articulate exactly when each is the right tool. Fluency with dimensional modeling: facts and dimensions, grain, surrogate keys, conformed dimensions, and slowly-changing-dimension patterns. Proven experience designing and operating a medallion architecture (bronze/silver/gold or equivalent layered model) — you know what belongs in each layer and how to keep the boundaries clean. Strong data lakehouse experience — open table formats (Delta, Iceberg, Hudi, or similar), schema evolution, partitioning, time travel, and the storage/compute separation — and the judgment to design for correctness and cost together. A track record of turning messy, inconsistent, multi-source input into a coherent, well-modeled, trustworthy layer — conforming, deduping, and resolving conflicts across sources that disagree. Experience with transformation frameworks and "analytics/pipelines as code" (dbt or similar): modular models, tests, documentation, and lineage under version control and code review. Engineering Craft 5+ years in data engineering, building and operating production data pipelines and warehouses — not just authoring queries. Expert SQL, plus strong coding ability in Python (or a comparable language) for pipelines, transformations, and tooling. Comfortable owning an existing orchestration and ELT/ETL stack (workflow schedulers, layered pipelines) and raising the bar on it rather than needing to invent one. Platform & Operations Comfortable owning pipeline reliability: idempotency, incremental and backfill loads, retries, and reconciliation — so data stays consistent and failures are caught, not silently dropped. Data-quality engineering: contracts, tests, freshness/volume checks, and anomaly detection at layer boundaries. Cost- and performance-aware: partitioning, clustering, file sizing, and query/compute tuning in a lakehouse or cloud-warehouse setting. Observability and lineage — you use production signals to catch drift and regressions early. Ownership & AI-Native Able to own the data platform end to end with limited guidance and establish patterns from scratch on real foundations. AI-native by default: you use AI tools to multiply your output — and verify what they produce. Preferred Skills Founding-team or early-stage experience standing up or re-architecting a data platform on a lean team. Streaming and real-time pipelines, change-data-capture (CDC), and event-driven ingestion. Proptech, real estate, fintech, or geospatial data experience — especially large, heterogeneous public and private datasets. Experience with cloud data platforms and lakehouse engines (e.g., Databricks, Snowflake, BigQuery, Spark, or similar). Semantic-layer / metrics-layer design and self-serve analytics enablement. Data governance, cataloging, and PII / access-control patterns for a multi-tenant data product. Contract testing between producers and consumers, and using production signals to catch model drift early. Ruxlo LLC is an Equal Opportunity Employer. AI disclosure: As part of our recruiting process, Ruxlo may use AI-assisted tools to help screen, review, and organize applications, as well as to support scheduling and communication. These tools assist our team and all final hiring decisions are made by humans. We work to ensure these tools are used fairly and without unlawful bias. If you have questions about how AI is used in your application, would like to request a reasonable accommodation or an alternative review process, please contact us at View phone number on click.appcast.io. #J-18808-Ljbffr