Sr. Manager, Data Engineering

Role Summary We're looking for a Senior Manager to lead a blended AI engineering team building the agentic AI platform and the marketing solutions that run on top of it. This is a delivery-leadership role: you own the roadmap, execution, and quality bar for a team of platform engineers, applied-AI solutions builders, and data engineering — turning cutting-edge agentic AI into reliable, production systems that marketing and analytics teams depend on every day.

You will operate at the intersection of platform and product: hardening the agentic infrastructure (LLM orchestration, retrieval, tool/MCP integration, deploys, reliability) while making sure the AI solutions built on it deliver real marketing outcomes. You'll grow the people on your team, set a high engineering bar, and partner across a global AI organization.

What You'll Do Platform & infrastructure

• Own delivery and reliability of the agentic AI platform: LLM orchestration, retrieval/RAG pipelines, tool and MCP integration, model routing, and evaluation.
• Drive engineering quality — test discipline, deployment safety, observability, cost and latency management — across everything the team ships.
• Set technical direction with your senior engineers; make the hard architecture calls and unblock the team (design-level involvement; not expected to write production code day-to-day).

Applied AI solutions

• Lead the team that builds AI agents and workflows for marketing use cases (analytics, paid media, content-to-intent, executive reporting) on top of the platform.
• Ensure solutions are grounded in real business outcomes and adopted by stakeholders — not demos that stall.
• Balance platform investment against solution delivery so both advance.

People leadership & delivery

• Manage, coach, and grow a blended team; run hiring to build out the function.
• Own the roadmap and quarterly planning; convert ambiguous priorities into committed, sequenced delivery.
• Represent the team's work to leadership and cross-functional partners; drive alignment across a globally distributed AI organization.

The Team You'll Lead A blended engineering team of:

• Agentic AI / platform engineers — Python, LLM orchestration, RAG, MCP/tooling, cloud-native deploys.
• Applied-AI solutions builders — wiring AI to marketing and analytics use cases.
• Data engineering — the pipelines and semantic layer the AI grounds on.

Must-Have Qualifications

• ~12+ years in software / AI/ML engineering, with 4+ years managing engineering teams (including hiring and growing engineers).
• Demonstrated depth in agentic AI / LLM systems — orchestration, retrieval/RAG, tool use, agent frameworks, and evaluation of AI quality.
• Track record of shipping production AI/ML systems at scale — reliability, deployment safety, cost/latency awareness — not just prototypes.
• Strong Python and modern cloud-native / containerized delivery.
• Ability to set a high engineering bar and make sound architecture decisions while leading primarily through the team.
• Excellent communication and stakeholder management across a globally distributed organization.

Preferred Qualifications

• Marketing technology or analytics domain experience — AEP / AJO / CJA, adtech, or digital marketing analytics.
• Experience with Databricks, vector databases (pgvector), graph stores (Neo4j), or similar data/AI infrastructure.
• Hands-on with agent/LLM frameworks and MCP, prompt/eval tooling, and LLM cost governance.
• Experience standing up or scaling a new AI engineering function.

What Success Looks Like (First 6–12 Months)

• A team operating with a clear roadmap, predictable delivery, and a visibly higher quality/reliability bar.
• At least one AI solution moved from concept to in-production use, adopted by marketing/analytics stakeholders.
• Platform reliability, evaluation, and cost/observability practices established as the default, not the exception.
• Key hires made; team members growing and taking on larger scope.

Leadership Behaviors

• Candor and clarity — lead with the bottom line, disagree openly, separate fact from guess, flag risks early.
• Ownership — own outcomes end-to-end, not just handoffs; the team ships things that work and says so honestly when they don't.
• Bar-raiser — hire people better than the current team and hold a high engineering standard.

Builder's bias — move fast on real problems, cut scope ruthlessly, prefer working systems over polished plans.

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