Manager - Engineering

Responsibilities

• 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

Stakeholder engagement & Cross team alignment

• Coordinate AIDF engineering priorities and resource allocation
• Manage GTM KG Engineering workload and strategy - working directly with JS and team
• Partner with PMM team for drafting table rollout
• Partner with CX Coworker pod for AIDF scoping
• Partner with Analyst / Intl DE org for QUBE rollout and strategy + AIDF MCP UAT
• Partner with ATS/DPAAS on Databricks administration and VOC
• Coordinate with marketing data owners and source-system teams
• Translate business requirements into engineering scope
• Escalate capacity, architecture, timeline and delivery risks
• Document key decisions for post-leave transition