The Opportunity
Adobe Express Data Platform is the intelligence backbone for millions of creators- a billion-event-per-day system spanning streaming, feature serving, agent data APIs, and a lakehouse that powers every personalization decision, experiment, and AI workflow. We are evolving it into a streaming-first, self-healing, agent-ready Lakehouse and we need engineers who challenge the status quo, move fast, and default to an agentic-first approach for every problem they encounter.
This is a systems-first engineering role. You won’t build ML models, you’ll build the foundational infrastructure that makes AI, analytics, and autonomous agents possible at scale. You’ll bring the conviction that any manual, repetitive, or slow platform workflow is a candidate for agentic automation and the engineering skill to make that real.
We are tackling hard, consequential problems: collapsing multi-hour pipeline latency to real-time, building MCP-compatible agent data APIs so autonomous AI systems can query and reason over platform data, evolving our ML Attribute Store with low-latency online feature serving, and pioneering AI-powered data governance that replaces manual operational toil with self-healing pipelines. Our team’s motto is simple: make the platform simpler, faster, and more reliable. Shipping fast isn’t reckless here - it’s a discipline.

What You’ll Do
Design and build streaming-first data pipelines that collapse end-to-end latency from hours to minutes, through event-driven architectures.
Own and extend the ML Attribute Store — building low-latency online serving capabilities alongside batch feature computation with unified batch/streaming aggregation to prevent training-serving skew.
Build MCP-compatible Agent Data APIs and tool servers that make the lakehouse discoverable and queryable by autonomous AI agents through standardized protocols, semantic layers, and catalog-driven data discovery.
Develop agentic framework — automated anomaly detection, duplicate event cleanup, transient event lifecycle management with audit trails, pipeline self-healing, and root cause analysis automation.
Drive operational excellence: observability, incident detection and response automation, performance tuning, cost optimization, and on-call ownership for mission-critical platform services.
Collaborate across Data Science, Personalization, Engineering Operations, Product, and Experimentation teams to translate platform capabilities into self-serve infrastructure that reduces engineering toil for non-platform teams.
Use and champion AI-powered developer tools (Claude Code, Cursor, GitHub Copilot, or similar) to accelerate personal and team engineering velocity.

What You Need to Succeed
Platform & Distributed Systems
6+ years of experience in data platform engineering, distributed systems, or backend infrastructure at scale.
Deep hands-on experience with Apache Spark, Databricks, Delta Lake, or equivalent lakehouse technologies (Iceberg, Hudi).
Proven track record building and operating large-scale pipelines processing billions of events daily with sub-hour latency SLAs.
Strong experience with streaming systems: Kafka, Kinesis, Flink, Spark Structured Streaming, or Delta Live Tables.
Proficiency in Python and/or Scala; SQL fluency required. Java or Go is a plus.
Experience with cloud platforms (AWS or Azure), containerization (Docker, Kubernetes), and CI/CD for data pipelines.

AI-Native Engineering & Agentic Systems
Production experience integrating LLMs into engineering workflows — not prototypes, but systems running against real data with real users. Includes prompt engineering, tool-use/function-calling, structured output parsing, and context window management.
Hands-on experience with agentic AI frameworks and multi-agent orchestration (LangChain, LangGraph, CrewAI, AutoGen, or custom agent loops with memory, planning, and tool routing).
Understanding of MCP (Model Context Protocol) and/or A2A protocols for exposing platform capabilities as agent-consumable tool servers — or demonstrable ability to build equivalent agent-tool integration surfaces.
Experience building or operating ML Feature Stores (online and/or offline), including training-serving skew mitigation, feature freshness trade-offs, and real-time feature computation.
Familiarity with RAG architectures: embedding generation, vector databases (FAISS, Pinecone, Weaviate, Databricks Vector Search), document chunking strategies, and retrieval evaluation.
Exposure to semantic layers, knowledge graphs, or metadata-driven data discovery systems (Unity Catalog, DataHub, OpenMetadata) that enable agents to autonomously navigate enterprise data catalogs.
Ability to build evaluation and feedback pipelines for AI systems — measuring agent accuracy, latency, cost attribution per workflow, and reliability at scale.
Demonstrated use of AI-powered developer tools (Claude Code, Cursor, GitHub Copilot, or similar) to accelerate engineering velocity.

Mindset & Working Style
Agentic-first instinct: you default to “can an agent do this?” before reaching for manual solutions, scripts, or traditional automation. You see every repetitive workflow as a target for autonomous replacement.
Challenger mentality: you question inherited architecture, push back on “we’ve always done it this way,” and drive fast improvement through first-principles thinking. You treat the status quo as technical debt.
Extreme bias for action and time-to-market: you ship iteratively, prefer “good enough now” over “perfect later,” and unblock yourself. You measure success in production impact, not design docs.
Systems thinker who traces dependencies, considers second-order effects, and asks “why did this break?” not just “how do I fix it?”
End-to-end ownership from design through production through 2 AM incident response. Platform reliability is personal.

Preferred Qualifications
Experience building AI-powered developer tools, self-serve data platforms, or code generation agents that reduce engineering toil.
Experience migrating batch-first data architectures to streaming-first without disrupting downstream consumers — including dual-write patterns, shadow pipelines, and incremental cutover strategies
Experience building autonomous monitoring systems that detect, diagnose, and remediate pipeline failures without human intervention — circuit breakers, auto-rollback, and intelligent retry logic
Familiarity with Adobe-native data and analytics solutions (CJA, AEP, Adobe Analytics) and data governance automation including FinOps practices, cost attribution, and compliance frameworks.
Contributions to open-source data or AI infrastructure projects, published engineering blog posts, or conference talks.
BS/MS in Computer Science, Engineering, or equivalent practical experience.

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.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com or call +1 408-536-3015.

AI Use Guidelines for Interviews:
Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

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 .

Expected Pay Range: Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $159,200 -- $301,600 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.

In California, the pay range for this position is $208,300 - $301,600

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-Specific Notices:
California :
Fair Chance Ordinances
Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

Colorado:
Application Window Notice

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

Massachusetts:
Massachusetts Legal Notice
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.