Senior Data Platform Engineer AI & Lakehouse

briowt; Glendale (CA); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Life Insurance

401K

Paid Vacation

Paid Holidays

Job summary

Brio Water Technology is seeking a hands-on Data Engineer to design, build, and operate end-to-end data pipelines and a Databricks lakehouse. You will own ingestion, governance, modeling, and ML/AI integrations, collaborating with cross-functional teams to deliver scalable, quality data products.

Responsibilities include building batch and streaming pipelines, developing a governed metrics layer, and deploying models with MLflow while ensuring robust security and auditing.

Qualifications

• 8+ years in data engineering with production cloud data platform ownership
• Hands-on with Databricks: Unity Catalog, Delta, Workflows, SQL warehouses
• Expert dbt and dimensional modeling with a governed metrics layer
• Strong SQL and Python; proficient with Git, CI/CD, IaC

Responsibilities

• Ingestion: design/build pipelines from SaaS apps, databases, APIs, and in-house systems
• Lakehouse and governance: manage a Databricks lakehouse with Unity Catalog
• Modeling: develop dimensional models and a governed metrics layer
• Unstructured data & ML: build pipelines turning media into structured data using transcription/OCR/LLM classification
• AI integration: implement LLM integrations with permissions and audit logging
• APIs: define requirements and build against application APIs
• Reporting: create governed dashboards/reports and alerting
• Observability: monitor pipeline freshness, data quality, and model performance

Skills

Databricks

Unity Catalog

SQL

Python

Data Modeling

CI/CD

Git

Delta Lake

Tools

dbt

Looker

Omni

Sigma

pgvector

Job description

Brio Water Technology is seeking a hands-on Data Engineer to design, build, and operate end-to-end data pipelines and a Databricks lakehouse. You will own ingestion, governance, modeling, and ML/AI integrations, collaborating with cross-functional teams to deliver scalable, quality data products.

Responsibilities include building batch and streaming pipelines, developing a governed metrics layer, and deploying models with MLflow while ensuring robust security and auditing.