Supply Chain Data Engineer

Zodiac Solutions, Inc; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Zodiac Solutions, Inc. seeks a Data Quality and Governance Lead to own DQ alerts, metrics, and lineage across our Databricks environment.

You will partner with analytics engineers to ensure the Gold layer meets certification standards, with robust anomaly detection and SLA driving data readiness for AI. You will define governance metrics, supervise data quality checks, and mentor analysts, data engineers, and BI developers as we modernize our data platform and implement certified datasets with

Qualifications

• Databricks hands-on experience with Gold-layer data models and Databricks, Delta Lake, lakehouse concepts.
• Ownership of data quality checks, anomaly detection, alerting, SLAs and root-cause analysis.
• Governance of metric definitions, semantic layer, metric views and data lineage.
• Led or contributed to data platform builds, migrations or modernization initiatives.

Responsibilities

• Own data quality alerting and issue management across Databricks pipelines, including SLAs and RCA.
• Define and enforce metric definitions, certification standards and governance gates.
• Collaborate with analytics engineers to govern the Databricks Gold layer and data lineage.
• Drive AI data readiness by scoring datasets for completeness and automation readiness.

Skills

Databricks hands-on

Data quality ownership

Metric governance

Solution design

Tools

Delta Lake

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].