Hiring! W2 Contract - Supply Chain Databricks Engineer - Bay Area, CA; onsite position

Propelsys Technologies LLC; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Propelsys Technologies LLC in the Bay Area seeks a Supply Chain Data Engineer to own data quality, governance, and the Databricks Gold layer. You will implement DQ checks, alerting, SLAs, and data lineage across the data platform to deliver reliable supply chain insights.

You will collaborate with analytics engineers to certify data models, drive AI readiness, and mentor teams on governance practices for scalable, auditable data assets.

Qualifications

• Own data quality alerting and issue management in Databricks with SLAs and root-cause analysis.
• Define metric definitions, calculations and governance standards; mentor analysts and BI developers.
• Govern Databricks Gold layer with certified data models and data lineage.
• Drive AI readiness through tiered data quality assessments and NLP-ready metric outputs.

Responsibilities

• Own data quality alerting, triage, routing, SLAs, and root-cause analysis in Databricks.
• Define and govern metrics, semantic layers, and data lineage across functions.
• Architect and govern the Databricks Gold layer with trusted data models and DQ checks.
• Lead data readiness for AI with completeness, accuracy, and automation assessments.

Skills

Databricks

Data quality ownership

Metrics governance

Data lineage

SQL

Tools

Delta Lake

Lakehouse

Job description

Role: Supply Chain Data Engineer

Location: Bay Area, CA; onsite position, no remote candidates

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.