P-1701
As an Engineering Manager on CustomerLake, you will lead the team building Profile Agents, shaping the product, architecture, and engineering foundation behind Databricks' Customer 360 platform.
CustomerLake is Databricks' new Agentic CDP, launched at Data + AI Summit 2026. This is our first move into agentic business applications, offering the data foundation of Marketing built natively into the Databricks Lakehouse. Early traction for this new product has been outstanding.
Profile Agents are a key foundation of this product. Building a trustworthy Customer360 has traditionally meant multi-year integration projects and seven or eight figures in professional services just to get a usable golden record. Profile Agents generate the pipelines, matching logic, and identity resolution needed to produce a Customer 360.
The product is early and still being defined, so you'll have real say over the product vision, architecture and how the team operates and scales.
The impact you will have- Own delivery for Profile Agents, from identity resolution and agentic pipeline generation through to production-quality Customer 360 outputs
- Hire and grow a small, senior team, and build the foundation it scales from as the product moves out of private preview
- Help set technical direction with product and the VP of Engineering, since much of what "good" looks like here is still undefined
- Build the quality bar for agent-generated pipelines: where agents act autonomously, where they need human review, and how we validate correctness against customer data
- Work directly with customers and design partners to help turn their Customer360 problems into engineering priorities
- You will have lots of room for growth by joining one of the fastest growing teams in one of the fastest growing Data + AI companies in the world
What we look for- 3+ years of engineering management experience leading high-performing engineering teams
- 8+ years of experience building and operating production data or distributed systems, with the hands-on depth to set a technical bar for the team
- Experience building or leading teams that ship data engineering products such as ETL, MDM, or data quality tooling, and an understanding of why Customer 360 projects are typically so expensive and slow
- Hands-on experience applying agents or LLMs to data engineering problems like matching, entity resolution, or pipeline generation, with a clear view on where agent autonomy helps versus where humans need to stay in the loop
- Genuine passion and prior experience taking a product from 0 to 1: comfortable defining process and architecture with no existing playbook
- Ideally, some understanding of how customer data is used downstream in marketing, sales, and service
- A track record scaling complex data services to thousands of enterprise customers, including the reliability and governance maturity that requires
- Experience managing multiple teams or other managers is a strong plus
- BS or higher in Computer Science or a related field, or equivalent experience
Pay Range TransparencyDatabricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected 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 anticipates 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.
Local Pay Range
$190,000-$261,250 USD
BenefitsAt Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
