Data engineer with Supply Chain

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

• Own data quality alerting and issue management in Databricks: alert coverage, triage, routing, SLAs, root-cause analysis and closure; keep DQ and abnormality checks current as pipelines change; report DQ health to stakeholders.
• Own metric definitions, calculations and measurement standards across functions; set certification standards and the governance gate metrics must pass before official reporting; mentor analysts, data engineers and BI developers.
• Work with analytics engineers to architect and govern the Databricks Gold layer: certified data models with embedded DQ and abnormality checks, metric views and data lineage.
• Drive data readiness for AI: tiered assessments scoring datasets on completeness, accuracy and automation readiness, and turn certified metrics into natural-language queryable experiences.
• Must-have skills
• Databricks hands-on, including curated or Gold-layer data models [Databricks, Delta Lake, lakehouse, medallion, Gold layer].
• Data quality ownership: DQ checks, anomaly or abnormality detection, alerting, SLAs, root-cause analysis [data quality, DQ, data observability, anomaly detection].
• Metric governance: metric definitions and certification, semantic layer or metric views, data lineage, data governance [metric definitions, semantic layer, metric views, data lineage, data catalog].
• Solutioning and transformation: has architected or led a data platform build, migration or modernization, not only operated pipelines [architect, solution design, transformation, migration, modernization].
• Nice-to-haves: supply chain or hardware operations data; AI readiness or natural-language query on metrics (text-to-SQL, AI/BI); mentoring or leading engineers; senior level by years and scope; profile signals open to work.