Supply Chain Data Engineer

TechDigital Group; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

TechDigital Group is seeking a senior data platform professional to own data quality, alerting, and governance across Databricks pipelines in San Francisco. You will lead end-to-end DQ ownership, triage alerts, and root-cause analysis while shaping the Gold layer with certified data models and data lineage.

You will mentor analysts and engineers and drive AI-ready data readiness. The role emphasizes architecture, modernization, and the establishment of solid metric definitions and semantic

Qualifications

• Databricks hands-on experience with Gold-layer data models.
• Own data quality checks, anomaly detection, alerting and SLAs.
• Define metrics, semantic layer, data lineage and governance.
• Architect or lead a data platform modernization project.

Responsibilities

• Own data quality alerting and issue management in Databricks; manage coverage, triage, routing and root-cause analysis.
• Define and govern metrics; ensure certification standards and governance gates are met.
• Collaborate to architect the Databricks Gold layer with lineage and certified data models.
• Drive data readiness for AI and translate metrics into NL queryable experiences.

Skills

Databricks hands-on

Data quality ownership

DQ checks

Anomaly detection

Alerting

SLAs

Root-cause analysis

Metric governance

Semantic layer

Data lineage

Data catalog

Data governance

Architect / solution design

Migration / modernization

Tools

Databricks

Delta Lake

Lakehouse

Gold layer tooling

Job description

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

• 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