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.

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours. 

Let’s Adobe together

At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

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At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.