Overview In this leadership role you drive complex Databricks-based data transformations from discovery to deployment, serving as the technical voice across pre-sales, delivery, and client advisory. You bridge client needs and feasible technical solutions, shaping architecture, governance, and accelerators that improve outcomes. You'll mentor engineers, contribute to practice capabilities, and represent Merkle's Databricks expertise with clients and partners. This is a hands-on, strategy-to-delivery position at scale, impacting multiple engagements and client relationships.
Compensation / Benefits- 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
Responsibilities- Lead pre-sales technical engagements (workshops, architecture reviews, assessments, POCs) to define data platform needs and Databricks fit
- Develop business cases and proposals quantifying Databricks value (ROI, speed-to-insight, cost avoidance)
- Build relationships with technical leaders and provide credible technical counsel
- Contribute to RFP responses and competitive positioning focused on technical differentiation
- Surface unmet technical needs during discovery and delivery and escalate to solutions architecture and leadership
- Represent Merkle's Databricks capability in client discussions as a trusted technical advisor
- Own technical relationships on architecture, feasibility, and risk with client data teams
- Deliver or lead complex data transformation programs anchored in Databricks; ensure quality and satisfaction
- Collaborate with Solutions Architects to validate designs before execution
- Create reusable Databricks implementations, reference architectures, and accelerators
- Conduct architecture and code reviews; contribute to standards and best practices
- Surface data quality issues and risks early; propose remediation and governance approaches
- Mentor engineers and grow team technical capability
- Maintain Databricks expertise (SQL, Python, Unity Catalog, DLT, AI capabilities) and stay aligned with product roadmap
- Advise on adjacent tooling and cloud platforms (AWS, GCP, Azure) and orchestration tools (Airflow, dbt)
- Build internal knowledge assets (playbooks, reference architectures) and participate in communities
- Mentor and develop engineers for stronger client-facing capabilities
- Lead discovery workshops with executive stakeholders; translate data platform investments into business outcomes
- Explain Databricks architecture to non-technical audiences with business context
- Produce thought leadership content and capability maturity models to position Merkle as an authority
- Represent Merkle in client forums and vendor conversations
- Embed AI tools in delivery and documentation to accelerate outcomes
Key requirements- 8+ years building data platform implementations on cloud data warehouses
- at least 3 years hands-on with Databricks (SQL, Python, DLT, Unity Catalog)
- Advanced SQL and Python for production-quality pipelines
- Deep experience in relational and lakehouse data modeling; Customer 360 and conformed data models
- Proven track record leading complex, multi-phase delivery programs
- Exceptional client-facing communication for both C-suite and technical audiences
- Demonstrated credibility with diverse client stakeholders
- Experience with data observability, pipeline automation, and production change control
- Regular use of AI coding assistants (GitHub Copilot, Cursor, Claude Code)
- Active use of cloud platforms (AWS, GCP, or Azure) with knowledge of cloud data architecture
- growth mindset
- advisory orientation
- collaborative leadership
- Databricks (Databricks SQL, Python, DLT, Unity Catalog)
- lakehouse patterns
- data modeling (Customer 360)
