Own end-to-end workforce transformation engagements with Databricks' largest customers to drive AI adoption.
Posted by employer 1 day ago
First seen on Joblaze 51 minutes ago
Last verified on the company career page 51 minutes ago
Not disclosed in this posting: work arrangement, visa sponsorship.
Joblaze summary
In this role, the practitioner leads comprehensive workforce transformation initiatives for Databricks' enterprise clients, focusing on bridging the gap between AI pilot projects and full organizational adoption. Key skills include change management, workflow assessment, and the ability to engage with C-suite executives to drive technology integration. Ideal candidates have extensive experience in organizational transformation, particularly in technology contexts, and a background in data or engineering roles. The position requires a hands-on approach to execution, emphasizing the importance of measurable behavior change tied to business outcomes.
Quick facts
- What's the salary range?
- Databricks lists $115,000–$197,550 for this role.
- How much experience is required?
- At least 8 years of relevant experience for this Data+AI Workforce Transformation Practitioner — Customer Delivery role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI, AI/ML, Data.
- What seniority level is this role?
- Databricks targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Data+AI Workforce Transformation Practitioner — Customer Delivery role at Databricks.
From the original posting
CSQ327R12
Enterprise customers are investing in AI, but most are stuck between a successful pilot and real organizational adoption. The gap is not technology. It is whether the workforce can change how it works.
You will own end-to-end workforce transformation engagements with Databricks' largest customers. You will diagnose where a customer's workforce is today, define what needs to change, build the transformation plan, and drive execution until adoption sticks. You are the directly responsible individual for each engagement, not a contributor to someone else's project.
The Impact You Will Have
- Own the full transformation lifecycle for each customer, from workforce assessment through capability pathways, operating rhythm, and measurement. You build the plan and you execute it.
- Run 2 to 3 concurrent engagements across different formats. These range from short-cycle assessments (2 to 4 weeks) to coached transformations (8 to 12 weeks) to deep embedded engagements (12 to 16 weeks). You will manage a mixed portfolio simultaneously.
- Design for multiple organizational layers within a single engagement. That means building executive sponsorship with CDOs, CIOs, and CHROs while also architecting the manager enablement and individual contributor adoption layers underneath.
- Drive execution, not just planning. You will stand up the operating rhythm, coach internal champions, unblock adoption barriers, and adjust the plan when reality diverges from the model.
- Design the human-agent collaboration model for each customer's workforce. Assess workflows and distinguish what should stay human from what should be automated, agent-assisted, or fully agent-led. Design differentiated pathways for builders in technical roles and users in workflow roles.
- Shape how engagements are scoped and priced. You are closest to the work and will ensure that commercial commitments are deliverable.
- Turn what you learn into repeatable tools and playbooks that can scale beyond practitioner-led delivery.
- Partner with technical deployment, AI transformation, and account teams. Other teams build and land the solution. You own what happens after.
What We Look For
- 8 or more years leading workforce or organizational transformation in a technology adoption context. You have executed transformations and owned outcomes, not just designed programs.
- You have led conversations with C-suite or VP-level sponsors and influenced how organizations adopt technology at scale.
- You have done the work, not just designed a curriculum about it. The strongest backgrounds include customer-side data or engineering leaders who drove transformation, former solutions architects or engineers who moved into transformation roles, and consultants with hands-on delivery experience.
- You can assess a workflow, identify what needs to change, and design the capability pathway to get there.
- You understand change management, including sponsorship models, adoption curves, resistance patterns, and how to build internal champions.
- You are comfortable with ambiguity. You will be building the playbook as you go, not executing a mature one.
- You are comfortable with commercial conversations, including engagement scoping, pricing input, and ensuring delivery commitments are realistic.
- Familiarity with data and AI platforms and the enterprise adoption challenges they create.
- A background in professional services, management consulting, or customer success is a plus.
- Experience measuring behavior change tied to business value, not just completions or satisfaction scores, is a plus.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
Zone 1 Pay Range
$143,700—$197,550 USD
Zone 2 Pay Range
$129,300—$177,750 USD
Zone 3 Pay Range
$122,200—$167,950 USD
Zone 4 Pay Range
$115,000—$158,050 USD
Standard company text repeated across Databricks's postings is omitted here.