Engineering lead (Databricks) Only W2

Job Description

Manage AIDF priorities, backlog, blockers, UAT and delivery risks

Review and approve pull requests on demand

Administer Databricks access, permissions and personal clusters

Serve as primary contact for ATS/DPAAS Databricks administrators

Direct GTM KG engineering delivery and dependencies

Continue Coworker integration and architecture scoping

Direct Eng resources / strategy Voice of Customer migration

Lead stakeholder and management level touch points for all the above

Skills required

• Databricks: catalogs, schemas, tables, jobs, clusters, serverless, AI capabilities, IAM, pipelines and repos
• SQL, Python, PySpark and REST APIs
• Front-end and back-end application development
• Data architecture, sourcing, transformations and pipeline design
• Knowledge graphs, MCP, Agent Builder and agent workflows
• Pull request review and engineering oversight

Domain knowledge

• AIDF architecture, roadmap and operating model
• GTM Knowledge Graph ingestion, serving and use cases
• Coworker context, integration and orchestration patterns
• Marketing data: campaign, paid media, web, email and CRM
• Marketing KPIs: impressions, CTR, conversions, MQL/SQL and pipeline attribution
• AEP data models and schema conventions
• Adobe Analytics and Adobe Target data structures
• Marketo, Dynamics, Salesforce, Third Party Vendor integrations
• Data governance, PII, GDPR and CCPA requirements
• Data partners, issue mitigation, lineage and transformation flows

Required access/Platforms used

• Databricks and associated administrative access
• Jira and source-control repositories
• AIDF and MCP environments
• Agent Builder, GTM STudio, BrAIns
• Github
• GH Copilot
• Azure Portal
• Workfront Planning
• AEP, Adobe Analytics and Adobe Target
• Marketo and Salesforce
• Teams, SharePoint and UAT tracking tools