Lead Forward Deployed Engineer, Databricks

This is a fully remote position, open to applicants in United States.

📋 Description

• Collaborate closely with business and technical stakeholders to pinpoint high-impact data and AI use cases that can be implemented on Databricks.

• Design, construct, and launch production-ready data and AI solutions utilizing Databricks features across the Lakehouse, Mosaic AI, Unity Catalog, Databricks SQL, Workflows, Delta Lake, Databricks Apps, Genie, Agents, and Lakebase.

• Facilitate client discovery sessions to comprehend business workflows, data accessibility, platform maturity, integration requirements, and measurable success criteria.

• Engineer AI-native data platforms that enable agentic workflows, semantic analytics, model deployment, retrieval systems, optimization models, and operational applications.

• Develop Genie rooms, semantic layers with Metric Views, decision-support applications, data products, AI applications, and agent memory architectures that assist clients in operationalizing insights and actions.

• Collaborate with data engineering, AI engineering, analytics, business, security, and governance teams to create secure, scalable, production-ready solutions.

• Produce prototypes, demos, technical reference architectures, and reusable accelerators that demonstrate the benefits of Databricks for enterprise AI and analytics tasks.

• Assist clients in modernizing data pipelines, enhancing platform architecture, applying governance patterns, and integrating AI systems into operational workflows.

• Partner with Aimpoint Digital’s alliance, sales, and delivery teams to cultivate Databricks-led opportunities and convert client needs into effective solution strategies.

• Generate thought leadership, solution accelerators, demos, and internal training materials that enhance Aimpoint Digital’s Databricks practice.

⛳️ Requirements

• Extensive experience in data engineering, AI engineering, platform engineering, solution architecture, or enterprise software development.

• Practical knowledge of Databricks, Spark, Delta Lake, Lakehouse architecture, data pipelines, model deployment, or modern data platform paradigms.

• Proficient in Python and SQL. Familiarity with PySpark, MLflow, Databricks Workflows, Unity Catalog, Databricks SQL, or comparable tools is highly preferred.

• Understanding of enterprise AI patterns such as RAG, agents, model serving, vector search, semantic layers, data applications, evaluation frameworks, and governance.

• Capability to engage directly with clients, discern ambiguous business requirements, and translate them into technical architecture and implementation strategies.

• Excellent communication skills with the ability to connect with executives, business leaders, architects, data engineers, ML engineers, and analytics teams.

• Comfortable transitioning from strategy to architecture to hands-on development.

• A practical grasp of the process required to transition from demo to production in complex enterprise settings.

• Databricks certification or significant hands-on delivery experience within the Databricks ecosystem (Preferred Qualifications).

🏝️ Benefits

• Health insurance

• Professional development opportunities