Lead Forward Deployed Engineer, Databricks
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Job Description
Lead Forward Deployed Engineer designing and deploying AI-native solutions in Databricks for business transformation. Working closely with clients to build production-grade data systems.
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
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
- Databricks certification or deep hands-on delivery experience in the Databricks ecosystem (Preferred Qualifications)
Benefits:
- Health insurance
- Professional development opportunities
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