Lead Solutions Engineer, Pre-Sales

This is a fully remote position, open to applicants in Illinois, +2 more states.

📋 Description

• Take charge of the technical aspects of Databricks initiatives from the initial client interaction until the engagement is signed and the transition to delivery is complete.

• Facilitate technical exploration with client engineering, architecture, data, analytics, AI, and executive stakeholders.

• Create designs for new Databricks platforms, migrations, modernization initiatives, and intricate data workloads.

• Define lakehouse and medallion architectures encompassing bronze, silver, gold, and downstream consumption tiers.

• Develop solutions for data ingestion, transformation, orchestration, governance, security, and semantic layer patterns.

• Construct data foundations that support BI, self-service analytics, Genie, ML, GenAI, RAG, and agentic AI applications.

• Convert technical requirements into project scope, delivery phases, assumptions, risks, resource plans, and estimates.

• Collaborate with sales and delivery leaders to craft proposals and statements of work.

• Create and present technical demonstrations, proofs of concept, architecture workshops, and solution presentations.

• Develop reusable pre-sales materials, including demo environments, reference architectures, discovery frameworks, solution patterns, technical content, and estimation models.

• Work closely with Databricks account teams and solution architects on joint initiatives and co-selling opportunities.

• Ensure seamless transitions from pre-sales to delivery.

⛳️ Requirements

• Over 7 years of experience in data engineering, platform engineering, solution architecture, technical consulting, or similar fields.

• Extensive experience in consulting, professional services, systems integration, or technical pre-sales roles.

• Proven track record of managing the technical workstream of complex enterprise sales cycles.

• Strong expertise in Databricks architecture and implementation within enterprise settings.

• In-depth knowledge of modern data engineering, including Spark, SQL, Python, data pipelines, orchestration, data modeling, and distributed processing.

• Solid understanding of lakehouse architecture, medallion patterns, Unity Catalog, Delta Lake, governance, and security.

• Experience with data platform or warehouse migrations.

• Familiarity with semantic layers, analytics architectures, and how enterprise data platforms support AI and ML workloads.

• Experience leading client discovery, architecture workshops, solution design, demos, estimation, and SOW scoping.

• Exceptional client-facing communication skills with engineers, architects, data leaders, and executives.

• Databricks certifications are preferred.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and continuous learning.

• Flexible work arrangements and a supportive work environment.

• Access to cutting-edge tools and technologies.