Sales and Delivery Architect, Director (L40)

The purpose of this role is to take database implementation projects through the entire development life cycle. This role will work as the solution lead and will be directly involved in the architecture, solution design, development, testing and deployment of the solution. This role serves as the technical subject matter expert for the solution.Job Description:The Director, Data Engineer – Technical Leadership & Advisory is a senior technical leadership role grounded in Databricks expertise, client advisory, and delivery excellence. You operate fluidly across pre-sales, discovery, and delivery—building deep credibility with clients by listening to technical challenges, sketching solutions in real time, and leading the delivery of complex data transformation programs. You are equally comfortable in a CMO's boardroom discussing data strategy outcomes and in a technical workshop whiteboarding a Databricks architecture with a client's data engineering team.You bridge the gap between what clients think they need and what they actually need. Your value comes from the combination of hands-on technical depth, clear business communication, and the ability to be present across the full engagement lifecycle. Clients and internal teams seek you out because you understand both the technical constraints and the business outcomes, and you have a track record of delivering on complex programs.This role is about deepening client relationships through demonstrated credibility, contributing to practice capability in data platforms, and mentoring internal talent. You help shape how Merkle positions and delivers Databricks work—both through the quality of your delivery and through the relationships you build with clients and partners.Key ResponsibilitiesClient Advisory & Pre-Sales Solutioning Participate in and lead pre-sales technical engagements (workshops, architecture reviews, assessments, POCs) that help clients understand their data platform needs and Databricks-fit Develop business cases and proposal content that quantifies the value of data platform and Databricks-specific initiatives (ROI, cost avoidance, speed-to-insight, etc.) Build and maintain strong relationships with technical decision-makers (CDOs, data leaders, platform owners) by providing credible, honest technical counsel Contribute to RFP responses and competitive positioning, with focus on technical differentiation and implementation feasibility Listen actively during discovery and delivery for unmet technical needs and opportunities; surface these to Solutions Architects, leadership, and client stakeholders Represent the firm's Databricks capability in client conversations; be a trusted technical voice in strategic discussions Technical Leadership & Delivery Own the technical relationship with client data leadership and engineering teams; be the trusted voice on architecture, feasibility, and risk Deliver or lead delivery of complex data transformation programs anchored in Databricks environments; ensure technical quality and client satisfaction Collaborate with Solutions Architects to validate and refine technical designs before execution; ensure feasibility assumptions hold in delivery Make day-to-day technical decisions on architecture, platform configuration, data modeling, and tooling within delivery engagements; explain tradeoffs clearly Design and build reusable Databricks implementations, reference architectures, and accelerators that accelerate future engagements Conduct architecture reviews, code reviews, and technical assessments; contribute to technical standards and best practices across the team Surface and escalate data quality issues, integration risks, and scope/schedule impacts proactively; propose remediation approaches As applicable, mentor or lead engineers on engagements; contribute to growing team technical capability Platform Expertise & Thought Leadership Maintain world-class expertise in Databricks: SQL, Python, Unity Catalog, Delta Live Tables, lakehouse patterns, AI/ML integration, and emerging Databricks AI capabilities Stay current on the Databricks product roadmap and emerging capabilities; advise clients on where Databricks is going and what that means for their architecture Evaluate and recommend data platform tooling adjacent to Databricks: cloud platforms (AWS, GCP, Azure), orchestration (Airflow, dbt), and consumption patterns Build and maintain Databricks certifications and thought leadership; consider active participation in Databricks communities, user groups, or speaking engagements Contribute to internal knowledge management: playbooks, reference architectures, and best-practice guides that become assets the practice reuses Mentor and develop engineers; elevate their technical and/or client-facing capability Client-Facing Communication & Advisory Lead technical discovery workshops with C-suite and director-level stakeholders; frame data platform investments in business outcome terms Explain complex Databricks archi