Databricks Data Quality & AI Readiness Engineer

Sharpedge Solutions Inc; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Sharpedge Solutions Inc seeks a senior data platform engineer to own data quality and governance in a Databricks-centric environment. You will define metrics, ensure SLAs, and mentor analysts, data engineers and BI developers.

You will work with analytics engineers to govern the Gold layer, implement DQ checks, data lineage and semantic views, and drive AI readiness by turning certified metrics into natural-language queryable experiences.

Qualifications

• Databricks hands-on experience with Gold-layer data models.
• Ownership of data quality checks, anomaly detection and alerts.
• Experience with governance, metrics definitions and data lineage.
• Ability to architect and lead data platform modernization projects.
• Mentoring analysts, data engineers and BI developers.

Responsibilities

• Own data quality alerting and issue management in Databricks, including triage, routing, SLAs, and root-cause analysis.
• Define metrics, calculations and standards; set certification and governance gate metrics; mentor analysts, engineers and BI developers.
• Collaborate to architect and govern the Databricks Gold layer with certified data models, DQ checks, metric views and lineage.
• Advance AI readiness with tiered data assessments and turn metrics into natural-language queryable experiences.

Skills

Databricks hands-on

Gold-layer data models

Data quality ownership

Anomaly detection

SLA management

Data governance

Data lineage

Semantic layer

Metric views

Data catalog

Architect design

Migration

Modernization

Mentoring engineers

Tools

Databricks

Delta Lake

Medallion architecture

Lakehouse

Job description

Sharpedge Solutions Inc seeks a senior data platform engineer to own data quality and governance in a Databricks-centric environment. You will define metrics, ensure SLAs, and mentor analysts, data engineers and BI developers.

You will work with analytics engineers to govern the Gold layer, implement DQ checks, data lineage and semantic views, and drive AI readiness by turning certified metrics into natural-language queryable experiences.