Sales and Delivery Architect, Director (L40)

dentsu•New York, NY

4d•$113,000 - $175,000•Remote

About The Position

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.

Requirements

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

Nice To Haves

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)
Experience with governance and Unity Catalog implementation at enterprise scale
Hands-on experience with modern data orchestration (Airflow, Prefect, dbt)
Contribution to pre-sales / discovery engagements; ability to articulate technical value propositions
Experience with adjacent platforms: Adobe AEP/RTCDP, Salesforce Data Cloud, or cloud analytics platforms
Mentoring experience; track record of growing junior engineers' technical and/or client-facing skills
Growth mindset: genuine curiosity about client business problems and appetite for learning new platforms and use cases
Advisory orientation: comfortable with ambiguity, comfortable offering recommendations even with imperfect information, comfortable taking educated risk in solutioning
Collaborative leadership: mentors and elevates team members; shares knowledge freely; does not hoard expertise
Bias toward clarity: produces clear documentation, architecture diagrams, and business case narratives; commits to making complexity accessible
Client advocate: willing to push back on aggressive timelines or scope while remaining collaborative; escalates issues early rather than late

Responsibilities

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
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
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