Data Engineer

Home Organizers; Glendale (CA); On-site; USD 200,000 - 250,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

401K Retirement Plan

Paid Vacation Time

Paid Holidays

Job summary

Home Organizers, Inc. in Glendale, CA, seeks a hands-on data engineer who will own the platform end-to-end—from ingestion to BI reporting and AI integration.

You will design, build, and operate batch and streaming pipelines, lakehouse governance, and a governed semantic layer, with strong emphasis on data quality and security. This role requires 8+ years in data engineering, deep Databricks experience, dbt, and the ability to partner with senior leaders to define metrics and trade-offs.

Qualifications

• 8+ years in data engineering, including ownership of a production cloud data platform end-to-end.
• Hands-on production experience with Databricks: Unity Catalog, Delta, Workflows/Lakeflow Jobs, and SQL warehouses.
• Expert dbt and dimensional modeling with a governed metrics layer.
• Strong SQL and Python. Comfortable with Git, CI/CD, and infrastructure as code.
• Built governed BI on a semantic model with row-level security and alerting.
• Delivered both managed-connector ingestion and custom pipelines with CDC and API sources.
• Shipped pipelines turning unstructured data into structured data using transcription, OCR, or vision models.
• Operated models in production with MLflow, including versioning and monitoring.
• Implemented data access controls and identity-provider based permissions.
• Experience with entity resolution across source systems.
• Written clear API requirements for data/AI integrations.
• Able to define metrics with senior business leaders and explain trade-offs.

Responsibilities

• Ingestion: design, build, and operate batch and streaming pipelines from SaaS apps, databases, APIs, and in-house systems.
• Lakehouse and governance: build and administer a Databricks lakehouse with Unity Catalog, row filters, and lineage.
• Modeling and semantic layer: develop dimensional models and a governed metrics layer; own testing and data quality.
• Unstructured data and ML: build pipelines for audio, video, documents, images; deploy and monitor models with MLflow.
• AI integration: build LLM integrations with read/write capabilities and audit logging.
• APIs: define integration requirements and build against them.
• Reporting: create governed dashboards and alerting on a semantic-model BI platform.
• Observability: monitor pipeline freshness and data quality with proactive alerts.

Skills

Data engineering

SQL

Python

Stakeholder communication

Cloud platforms

Tools

Databricks Unity Catalog

Delta Lake

dbt

MLflow

Looker/Sigma/Omni

Git

CI/CD

Airbyte/Fivetran

Job description

About this position

About the company

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.

About the role

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.

What you’ll do

• 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.

Qualifications

• 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 (MetricFlow, 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.
• Delivered both managed-connector ingestion (Lakeflow Connect, Fivetran, Airbyte) and custom pipelines, including CDC and API sources.
• Shipped at least one pipeline that turns unstructured data (audio, documents, or images) into structured data using transcription, OCR, LLM classification, or vision models.
• Operated models in production with MLflow or an equivalent registry, including versioning, scoring jobs, and monitoring.
• Implemented data access controls: row filters, column masks, PII handling, and group-based permissions synced from an identity provider.
• Solved entity resolution across multiple source systems (customers, locations, or employees).
• Built integrations that write back to production systems (in-house applications, CMS, or SaaS APIs) with authorization checks, idempotency, rollback, and audit logging.
• Consumed APIs built by other teams and written clear requirements for what a data or AI integration needs from them.
• Able to work directly with senior business leaders to define metrics and explain trade-offs in plain language.
• Must be located around Glendale, CA as this role is 5 days on-site.

Strongly Preferred

• Built LLM applications on governed MCP servers, text-to-SQL with an evaluation set, and retrieval systems covering embedding models, chunking strategy, hybrid (keyword plus vector) search, metadata and permission filtering, and retrieval evaluation, using Databricks Vector Search, pgvector, or similar.
• Experience with enterprise search or knowledge platforms (Glean or similar), knowledge graphs, or business glossaries.
• Data observability tooling (Metaplane, Elementary, Monte Carlo, Lakehouse Monitoring).
• Change-management automation: preview environments, automated tests, approval workflows, and policy-as-code (OPA, Cedar, or similar).
• Streaming and IoT sensor or edge-device telemetry landed through Structured Streaming, Kafka, or edge-to-cloud pipelines.
• Multi-cloud work across AWS, Azure, and GCP.
• Marketing and advertising data (Google Ads, Meta, GA4, call tracking).
• Home services, franchise, field service, or retail data.
• Working knowledge of SOC 2 controls and California privacy requirements (CCPA/CPRA).

We believe in recognizing and rewarding our employees for a job well done. We offer growth potential for motivated individuals, competitive compensation, and a comprehensive benefits package, including:

• Medical, Dental, Vision, Life Insurance
• 401K Retirement Plan
• Paid Vacation Time
• Paid Holidays
• and More!

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The pay range for this role is:

200,000 - 250,000 USD per year(Glendale)