Engineering Manager: Customer 360 & Profile Agents

Databricks

New York (NY)

On-site

USD 190,000 - 261,000

Full time

14 days+

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Job summary

Databricks is seeking an Engineering Manager for CustomerLake to lead Profile Agents, shaping product, architecture, and the engineering foundation of Customer 360. You will own delivery, hire senior engineers, and define technical direction while collaborating with customers to translate problems into engineering priorities.

The role requires strong leadership, deep data systems experience, and hands-on work with agents/LLMs.

Qualifications

  • 3+ years of engineering management experience leading high-performing teams.
  • 8+ years building and operating production data or distributed systems.
  • Experience building or leading data engineering products such as ETL, MDM, or data quality tooling.
  • Hands-on experience applying agents or LLMs to data engineering problems like matching and identity resolution.
  • Track record taking a product from 0 to 1 with no existing playbook.
  • BS or higher in Computer Science or related field or equivalent experience.

Responsibilities

  • Own delivery for Profile Agents from identity resolution to production-quality outputs.
  • Hire and grow a small senior team and scale its foundation as the product matures.
  • Help set technical direction with product and VP of Engineering.
  • Build the quality bar for agent-generated pipelines and validation against customer data.
  • Work with customers and design partners to translate Customer360 problems into priorities.

Skills

Engineering management
Data engineering
Agent/LLM experience
Product from 0 to 1
Team leadership

Education

BS in Computer Science or related field

Job description

Databricks is seeking an Engineering Manager for CustomerLake to lead Profile Agents, shaping product, architecture, and the engineering foundation of Customer 360. You will own delivery, hire senior engineers, and define technical direction while collaborating with customers to translate problems into engineering priorities.

The role requires strong leadership, deep data systems experience, and hands-on work with agents/LLMs.

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