Supply Chain Data Engineer(W2 only)

XChange Software Inc; San Francisco (CA); On-site; USD 140,000 - 190,000; Part time

Job summary

XChange Software Inc is seeking a Supply Chain Data Engineer to own data quality and governance across Databricks-based pipelines in the Bay Area. This 6- to 12-month W2 role focuses on building Gold-layer data models, defining metrics, and enabling AI-readiness through robust data certification and lineage.

You will mentor analysts and engineers, implement DQ checks with SLAs, and collaborate with analytics teams to deliver trusted, scalable data services suitable for enterprise-scale supply

Qualifications

• Databricks hands-on with Gold-layer data models.
• Data quality ownership including checks, anomaly detection, alerts and SLAs.
• Metric definitions, semantic layer, data lineage and governance responsibilities.
• Experience with solution design, transformation, migration, or modernization of data platforms.

Responsibilities

• 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.

Skills

Databricks hands-on

Data quality ownership

Metric governance

Solution architecture

Tools

Databricks

Delta Lake

Data governance tools

Job description

Role: Supply Chain Data Engineer (W2 only (No C2C))

Location: Bay Area, CA;

6-12 Months

• 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

a. Databricks hands-on, including curated or Gold-layer data models [Databricks, Delta Lake, lakehouse, medallion, Gold layer].

b. Data quality ownership: DQ checks, anomaly or abnormality detection, alerting, SLAs, root-cause analysis [data quality, DQ, data observability, anomaly detection].

c. 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].

d. 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.