销售与交付架构师,总监(L40)
Sales and Delivery Architect, Director (L40)
该职位的职责是将数据库实施项目贯穿整个开发生命周期。该职位将作为解决方案负责人,直接参与解决方案的架构、方案设计、开发、测试和部署。该职位是解决方案的技术专家。
职位描述:
数据工程师总监 – 技术领导与咨询是一个基于Databricks专业知识、客户咨询和交付卓越的高级技术领导职位。您将在售前、发现和交付阶段流畅运作,通过倾听技术挑战、实时绘制解决方案,并领导复杂的数据转换项目交付,与客户建立深厚信任。您既能坐在首席营销官的会议室中讨论数据战略成果,也能在技术研讨会中与客户的数据工程团队一起白板演示Databricks架构。
您能够弥合客户认为他们需要什么与他们真正需要什么之间的差距。您的价值来自于实际技术深度、清晰的业务沟通能力,以及在整个项目生命周期中保持参与的能力。客户和内部团队都会寻求您的帮助,因为您既理解技术限制,也关注业务成果,并且有成功交付复杂项目的记录。
该职位旨在通过展示的信任加深客户关系,为数据平台实践能力做出贡献,并指导内部人才成长。您帮助塑造Merkle如何定位和交付Databricks工作——既通过您交付的质量,也通过您与客户和合作伙伴建立的关系。
主要职责
客户咨询与售前解决方案
- 参与并主导售前技术活动(研讨会、架构评审、评估、POC),帮助客户了解其数据平台需求和Databricks适配性
- 制定商业案例和提案内容,量化数据平台和Databricks特定项目的价值(ROI、成本避免、洞察速度等)
- 通过提供可信、诚实的技术建议,与技术决策者(CDO、数据领导者、平台负责人)建立并维护牢固关系
- 贡献于RFP响应和竞争定位,重点在于技术差异化和实施可行性
- 在发现阶段积极倾听并深入理解客户需求
查看英文原文
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 Responsibilities
Client 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 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
What Success Looks Like
- 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
Qualifications
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
Strongly Preferred
- 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
Cultural / Behavioral
- 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
The annual salary range for this position is $113,000 - $175,000. Placement within the salary range is based on a variety of factors, including relevant experience, knowledge, skills, and other factors permitted by law.
Benefits available with this position include:
- Medical, vision, and dental insurance,
- Life insurance,
- Short-term and long-term disability insurance,
- 401k,
- Flexible paid time off,
- At least 15 paid holidays per year,
- Paid sick and safe leave, and
- Paid parental leave.
Dentsu also complies with applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, in accordance with its plans and policies. For further details regarding Dentsu benefits, please visit www.dentsubenefitsplus.com.
To begin the application process, please click on the “Apply” button at the top of this job posting. Applications will be reviewed on an ongoing basis, and qualified candidates will be contacted for next steps.
Location:
USA - Remote - New YorkBrand:
MerkleTime Type:
Full timeContract Type:
Permanent
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Originally posted on Himalayas