Lead Databricks Data Engineer (Supply Chain)

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

Job summary

Galent is seeking a Senior Supply Chain Data Engineer to drive data quality, governance, and analytics architecture on a Databricks-based platform. You will lead data quality monitoring, observability, and governance across the data landscape to support supply chain operations.

The role requires hands-on Databricks expertise, experience with Lakehouse/Medallion architectures, and mentoring teams on governance best practices for enterprise reporting.

Qualifications

• Strong hands-on experience with Databricks and Lakehouse architectures.
• Experience with data governance, data quality frameworks, and large-scale data transformations.
• Mentor analysts, BI developers, and data engineers on governance best practices.
• Experience defining and governing enterprise-wide metric definitions.
• Architect Databricks Gold Layer and certified data models.

Responsibilities

• Own end-to-end data quality monitoring and issue management within Databricks.
• Design and maintain data quality alerts, anomaly detection, and observability frameworks.
• Manage issue triage, routing, SLA adherence, root-cause analysis, and resolution.
• Ensure data quality and abnormality checks align with evolving data pipelines.
• Provide regular reporting on data quality health and compliance metrics to stakeholders.
• Define and govern enterprise-wide metric definitions, calculations, and measurement standards.
• Establish certification criteria and governance controls for production reporting.
• Drive metric consistency across business and technical teams.
• Mentor analysts, BI developers, and data engineers on governance best practices.
• Partner with analytics engineering teams to architect and govern the Databricks Gold Layer.
• Develop certified data models with embedded: Anomaly detection mechanisms; Metric views.
• Support enterprise data governance and reporting initiatives.
• Lead data readiness assessments for AI and advanced analytics initiatives.
• Accuracy; Consistency; Automation readiness; Enable natural language access to certified metrics and reporting assets.

Skills

Databricks

Lakehouse Architecture

Medallion Architecture

Data Quality Frameworks

Data Observability

Anomaly Detection

Alerting & Monitoring

SLA Management

Metric Definitions

Semantic Layer Design

Metric Views

Data Catalogs

Data Platform Modernization

Data Migrations

Tools

Databricks

Job description

We are seeking a senior Supply Chain Data Engineer to drive data quality, metric governance, and analytics architecture initiatives within a Databricks-based data platform. The ideal candidate will have strong hands‑on experience with Databricks, data governance, data quality frameworks, and large‑scale data transformation programs supporting supply chain operations.

Key Responsibilities

Data Quality & Observability

• Own end‑to‑end data quality monitoring and issue management within Databricks.
• Design and maintain data quality alerts, anomaly detection, and observability frameworks.
• Manage issue triage, routing, SLA adherence, root‑cause analysis, and resolution.
• Ensure data quality and abnormality checks remain aligned with evolving data pipelines.
• Provide regular reporting on data quality health and compliance metrics to stakeholders.

Metric Governance & Standards

• Define and govern enterprise‑wide metric definitions, calculations, and measurement standards.
• Establish certification criteria and governance controls for production reporting.
• Drive metric consistency across business and technical teams.
• Mentor analysts, BI developers, and data engineers on governance best practices.
• Partner with analytics engineering teams to architect and govern the Databricks Gold Layer.
• Develop certified data models with embedded:
• Anomaly detection mechanisms
• Metric views
• Support enterprise data governance and reporting initiatives.

AI & Data Readiness

• Lead data readiness assessments for AI and advanced analytics initiatives.
• Accuracy
• Consistency
• Automation readiness
• Enable natural language access to certified metrics and reporting assets.

Required Skills & Experience

Databricks & Lakehouse Architecture

• Strong hands‑on experience with:
• Databricks
• Lakehouse Architecture
• Medallion Architecture

Data Quality & Observability

• Data Quality (DQ) Frameworks
• Data Observability
• Anomaly Detection
• Alerting & Monitoring
• SLA Management

Data Governance & Metrics

• Experience with:
• Metric Definitions & Certification
• Semantic Layer Design
• Metric Views
• Data Catalogs

Data Platform Transformation

• Experience leading or architecting:
• Data Platform Modernization
• Data Migrations

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.

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