Data Engineer

Home Organizers, Inc. is the parent organization behind a portfolio of well-known home products and services brands, including Closet World, Closets by Design, Brio Water Technology, and others. With decades of experience supporting innovation, design, manufacturing, and customer focused solutions, Home Organizers helps its brands deliver high quality products and services that improve everyday living for households across the country.

Brio Water Technology, a Home Organizers, Inc. company, is a market leading water products brand that has helped millions stay hydrated through its unique and innovative product line. We offer full home water solutions designed to elevate the way people hydrate, combining sophisticated technology with modern, top tier design to deliver exceptional performance, customer satisfaction, and enhanced functionality.

We are hiring a strong, hands‑on data engineer who works across the full stack, from source ingestion and pipelines through modeling, governance, AI integration, and the reporting layer. You will design, build, and operate the platform yourself. We may add engineers later; the standards you set are the ones they will inherit.

• Judgment is the job. You weren't hired just to execute. Be judicious. Weigh the cost‑benefit against risk on every build‑versus‑buy decision and every shortcut.
• Ship with discipline. Every pipeline, model, and metric ships with tests, documentation, version control, and a named owner.
• Influence over authority. Much of the work depends on partners across the business. You earn their partnership. Run your big calls past me, then move.

• Ingestion . Design, build, and operate batch and streaming pipelines from SaaS applications, databases, APIs, and custom in‑house systems, using managed connectors where they fit and custom code (including change data capture) where they do not.
• Lakehouse and governance . Build and administer a Databricks lakehouse (medallion architecture, Unity Catalog), including row filters, column masks, tagging, lineage, and identity‑provider group sync.
• Modeling and semantic layer . Develop dimensional models, entity resolution, and a governed metrics layer in dbt. Own testing, documentation, and data quality.
• Unstructured data and ML . Build pipelines that turn audio, video, documents, images, and sensor or edge‑device telemetry into structured data using transcription, OCR, LLM classification, and computer vision. Deploy, version, and monitor models with MLflow; build and maintain vector search indexes.
• AI integration . Build and maintain LLM integrations (MCP servers, retrieval, text‑to‑SQL) with both read and write capabilities, enforcing user‑level permissions, approval workflows, and audit logging.
• APIs . Define integration requirements for APIs built by our application engineering team, and build against them.
• Reporting . Build governed dashboards, reports, scheduled delivery, and alerting on a semantic‑model BI platform.
• Observability . Monitor pipeline freshness, data quality, model performance, and index health, with alerting that surfaces problems early.

• 8+ years in data engineering, including at least 3 owning a production cloud data platform end to end, from ingestion through the reporting layer.
• Hands‑on production experience with Databricks:
  Unity Catalog, Delta, Workflows/Lakeflow Jobs, and SQL warehouses.
• Expert dbt and dimensional modeling: layered projects, tests, documentation, and a semantic or metrics layer (Metric Flow, Metric Views, or LookML).
• Strong SQL and Python. Comfortable with Git, CI/CD, and infrastructure as code.
• Built governed BI on a semantic model (Omni, Sigma, Looker, or similar) with row‑level security, scheduled delivery, and alerting.
• Deliver…