Lead Forward Deployed Engineer, Databricks 2026- US, UK

As a Lead Forward Deployed Engineer – Databricks, you will work directly with clients to design, build, and operationalize AI and data solutions on the Databricks platform.

This role is for someone who can operate at the edge of the customer environment: understanding the business problem, assessing the data and platform architecture, designing the right technical path, and building production-grade solutions using Databricks capabilities.

You will work across modern data engineering, AI engineering, agentic workflows, semantic analytics, Lakehouse architecture, Databricks Apps, Lakebase, Genie, Mosaic AI, model serving, and production deployment patterns.

The role requires a builder’s mindset, strong client presence, and the ability to turn ambiguous business needs into deployed technical solutions.

• →Work directly with business and technical stakeholders to identify high-value data and AI use cases that can be delivered on Databricks
• →Design, build, and deploy production-grade data and AI solutions using Databricks capabilities across the Lakehouse, Mosaic AI, Unity Catalog, Databricks SQL, Workflows, Delta Lake, Databricks Apps, Genie, Agents, and Lakebase
• →Lead client discovery sessions to understand business workflows, data availability, platform maturity, integration needs, and measurable success criteria
• →Architect AI-native data platforms that support agentic workflows, semantic analytics, model deployment, retrieval systems, optimization models, and operational applications
• →Build Genie rooms, semantic layers using Metric Views, decision-support applications, data products, AI applications, and agent memory architectures that help clients operationalize insight and action
• →Partner with data engineering, AI engineering, analytics, business, security, and governance stakeholders to design secure, scalable, production-ready solutions
• →Create prototypes, demos, technical reference architectures, and reusable accelerators that showcase the value of Databricks for enterprise AI and analytics workloads
• →Help clients modernize data pipelines, improve platform architecture, implement governance patterns, and deploy AI systems into operational workflows
• →Work with Aimpoint Digital’s alliance, sales, and delivery teams to shape Databricks-led opportunities and translate client needs into winning solution approaches
• →Develop thought leadership, solution accelerators, demos, and internal enablement materials that strengthen Aimpoint Digital’s Databricks practice

• Strong experience in data engineering, AI engineering, platform engineering, solution architecture, or enterprise software development
• Hands-on experience with Databricks, Spark, Delta Lake, Lakehouse architecture, data pipelines, model deployment, or modern data platform patterns
• Strong Python and SQL skills. Experience with PySpark, MLflow, Databricks Workflows, Unity Catalog, Databricks SQL, or similar tooling is strongly preferred
• Familiarity with enterprise AI patterns such as RAG, agents, model serving, vector search, semantic layers, data applications, evaluation frameworks, and governance
• Ability to work directly with clients, understand ambiguous business needs, and translate them into technical architecture and implementation plans
• Strong communication skills with the ability to engage executives, business leaders, architects, data engineers, ML engineers, and analytics teams
• Comfort moving from strategy to architecture to hands-on development
• A practical understanding of what it takes to move from demo to production in complex enterprise environments

• Databricks certification or deep hands-on delivery experience in the Databricks ecosystem
• Experience building Databricks Apps, Genie rooms, Lakebase-backed applications, Mosaic AI workflows, feature pipelines, MLflow deployments, vector search systems, or agentic solutions
• Experience with cloud platforms such as AWS, Azure, or GCP
• Experience in consulting, forward deployed engineering, solution architecture, field engineering, technical pre-sales, or client-facing delivery
• Experience designing governed data products, semantic models, operational analytics applications, or AI/ML systems
• Familiarity with industry use cases in retail and CPG, manufacturing, supply chain, energy, AI infrastructure, or private equity
• Ability to create technical architecture diagrams, delivery roadmaps, demos, sales enablement assets, and reusable solution accelerators

Questions You Will Help Clients Answer

• How do we turn Databricks into an enterprise AI platform rather than just a data platform?
• Which business workflows can be improved using Genie, Agents, Lakebase, Databricks Apps, or Mosaic AI?
• How should we structure our Lakehouse architecture to support AI-native applications?
• How do we build reliable agent memory, governed data products, semantic analytics, and production AI applications on Databricks?
• How do we move from migration and platform setup into measurable business outcomes?
• How do we design data and AI systems that are secure, governed, scalable, and operationally useful?

The future of the Databricks partner ecosystem will not be won by firms giving away generic offshore services. It will be won by partners who can help clients identify the right business problems, architect the right platform patterns, and build production AI and data systems that executives can trust. Aimpoint Digital is building that model.

As a Lead Forward Deployed Engineer focused on Databricks, you will help clients move beyond implementation and into transformation, leveraging Databricks as the foundation for building, deploying, and governing enterprise AI at scale.