Sales and Delivery Architect, Director (L40)

Required (Core)

• 8+ years building data platform implementations on cloud data warehouses, with at least 3 years of hands-on experience with Databricks (including SQL, Python, DLT, Unity Catalog, or equivalent scope)
• Advanced SQL and Python; ability to code production-quality data pipelines without oversight
• Deep experience with relational and lakehouse data modeling; design of Customer 360 and conformed data models
• Demonstrated experience participating in and leading complex, multi-phase delivery programs; clear track record of technical delivery success
• Exceptional client-facing communication skills; comfortable presenting technical concepts to both C-suite and technical audiences
• Demonstrated ability to build credibility and trust with client stakeholders across technical and business functions
• Experience with data observability, pipeline automation, and formal change control in production environments
• Demonstrated use of AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a regular productivity tool
• Active use of cloud platforms (AWS, GCP, or Azure); working knowledge of cloud data architecture
• Databricks Certified Data Engineer (Professional level) or Databricks Solutions Architect Certification
• Active engagement with Databricks thought leadership: community participation, conference speaking, or knowledge-sharing
• Experience with Databricks AI capabilities (Databricks AI Functions, vector search, or Mosaic AI)
• 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
• Lead technical discovery workshops with C-suite and director-level stakeholders; frame data platform investments in business outcome terms
• Explain complex Databricks architecture and data engineering decisions to non-technical audiences (CMOs, CFOs, board members) with clarity and business context
• Produce thought leadership and advisory content: POV papers, assessment frameworks, and capability maturity models that position Merkle and yourself as authorities
• Represent the firm in client forums, advisory board meetings, and steering committees where data platform strategy is discussed
• Large, complex Databricks delivery engagements delivered on time, on budget, with high client satisfaction and clear technical governance
• A reputation within key client accounts as the trusted technical voice—someone clients ask for and rely on to solve complex data infrastructure problems
• Deep relationships with client technical and business stakeholders; clients and internal teams seek you out for solutioning conversations and technical advice
• Reusable Databricks implementations, reference architectures, and accelerators that the practice leverages to compress timelines and improve delivery quality
• Contributions to pre-sales engagements that demonstrate technical credibility and help shape how Merkle positions Databricks capabilities
• Internal mentorship and knowledge-sharing; engineers grow in technical capability and client-facing confidence through exposure to your work
• Demonstrable depth in Databricks: current certifications, awareness of product roadmap and emerging capabilities, and ability to articulate why decisions matter
• Representation of Merkle's Databricks expertise both internally and externally (internal forums, client presentations, vendor conversations)
• AI tools embedded in your technical workflow and actively used to accelerate delivery and documentation