Senior Software Engineer, Data

• 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.

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.

• 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.

• 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.

• 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.

• 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 (848) 321-5588.