Senior Forward Deployed Engineer (Technical Data Architect)
Works on data+AI productionization with Databricks and Spark, using MLOps and AI APIs — supports rapid prototyping and deploying AI services.
About the Role
Senior Forward Deployed Engineer (Technical Data Architect) at Databricks based in London (hybrid). Own architecture and deliver production-grade data and AI solutions for customers, leading design, implementation, and operationalization across data engineering, ML/AI and application layers.
Job Description
Role
Databricks is hiring a Senior Forward Deployed Engineer (Technical Data Architect) in London (hybrid). This is a hands-on, customer-facing role that leads architecture and delivery of production-grade data and AI solutions on the Databricks platform, combining data engineering, ML/AI model integration, and application development.
Key Responsibilities
- Lead production solution delivery: design and build reference architectures, custom applications, data ingestion pipelines, and ML/AI model integration.
- Own architecture and design decisions to ensure solutions are secure, scalable, and aligned with customer needs and Databricks best practices.
- Drive transformational big data and AI projects end-to-end: design, build, deploy, and operationalize industry-leading applications.
- Embed with customer teams, engage stakeholders from technical ICs to executives, and provide training and technical enablement.
- Work cross-functionally with engineering, product, project management, and customer support to scope engagements, deliver technical components, and escalate product/implementation feedback.
- Contribute reusable assets (accelerators, frameworks, best practices) to scale impact across accounts and influence the product roadmap.
- Travel to customer sites approximately 20% of the time.
Requirements
- Significant experience in data engineering, data platforms & analytics, or software/full-stack engineering.
- Proficiency writing code in one or more of: Python, Scala, JavaScript/TypeScript and familiarity with modern frameworks.
- Working knowledge of at least two cloud ecosystems (AWS, Azure, GCP) and in-depth expertise in at least one.
- Deep experience with distributed computing and Apache Spark, including knowledge of Spark runtime internals.
- Familiarity with CI/CD practices for production deployments and experience with MLOps, ML/AI models and AI APIs.
- Experience designing and deploying performant end-to-end data architectures combining pipelines, ML/AI models, and user-facing interfaces.
- Strong technical project delivery skills: scoping, managing timelines, translating complex concepts into actionable solutions, documentation and whiteboarding.
- Experience working with enterprise clients and managing a broad stakeholder range; strong customer empathy and communication skills.
- Willingness to travel to customers ~20% of the time. Databricks certification is an advantage.