Lead Forward Deployed Engineer, Databricks 2026- US, UK

Aimpoint Digital•Atlanta, GA

1d•Remote

About The Position

Aimpoint Digital is a market-leading data, AI, and operations research advisory and solution engineering firm. We help organizations build modern data platforms, enterprise AI systems, decision intelligence solutions, optimization models, and production analytics capabilities. We are one of the strongest technical partners in the Databricks ecosystem, with deep expertise across platform engineering, data engineering, AI engineering, MLOps, operations research, and applied analytics. Our teams help clients move beyond platform implementation into real business transformation. As Databricks continues to become a core enterprise AI platform, we are hiring Forward Deployed Engineers who can help clients design and deploy AI-native solutions on Databricks that create measurable business impact. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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