Role Description & Responsibilities
Data Engineering
About Databricks
Databricks is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Data Engineering role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
We're hiring an AI Automation Engineer to build the next generation of IT automations to improve the employee experience at Databricks.
You'll be building production-grade AI agents and automation services that understand employee requests, retrieve enterprise context, reason across systems, invoke tools and APIs, and take action safely. The goal isn't to automate existing workflows the same way we've done it for the past decade. It's to reimagine how employees interact with IT through intelligent, contextual, and increasingly agentic experiences and to do it on Databricks.
Key Responsibilities & Day-to-Day Scope
- ✓ Build AI agents and automations: Design, deploy, and run AI agents that support all of our employees. The agents interpret natural-language requests, gather context, select tools, execute actions, and communicate outcomes.
- ✓ Maintain MCP servers across our AI tools portfolio.
- ✓ Build agentic workflows that decompose goals into steps, handle failures, pause for human approval as needed, and resume their workflows.
- ✓ Ground agents in trusted internal knowledge (policies, runbooks, system documentation, SOPs, etc).
- ✓ Turn high-volume support CUJs into self-service experiences: Identify repetitive, high-friction IT interactions and convert them into reliable AI automation experiences.
- ✓ Instrument AI automations with logs, metrics, and feedback loops.
- ✓ Design and implement AI automations with identity governance, tools RBAC, human-in-the-loop approvals where applicable, audit trails, and failure handling.
- ✓ Create shared automation infrastructure (credential vaults, event ingestion, scheduling, approval workflows, cache/state stores) that other teams can build on.
- ✓ Work with IT Support, Employee Experience, Security, IAM, Infrastructure, and Engineering to identify automation opportunities and take solutions from prototype to production.
- ✓ Write and maintain high-quality technical/engineering documentation that clearly communicates system designs, operational procedures, and architectural decisions.
- ✓ Proficiency in Python or Go [3yrs].
- ✓ Experience with Git-based development, automated testing, and CI/CD.
- ✓ Strong experience with REST APIs, OAuth2/authentication frameworks, event-driven integrations, and enterprise system integrations.
Official Qualifications & Technical Requirements
- • Shipped production LLM-powered applications or AI agents.
- • Experience with agent patterns: tool/function calling, multi-step workflows, context management, RAG, structured outputs, and plan decomposition.
- • Understanding of LLM failure modes: hallucination, incorrect tool use, ambiguous intent, prompt injection, excessive permissions, and non-deterministic behavior.
- • Experience evaluating and improving AI systems using automated evals, traces, test datasets, human feedback, or similar approaches.
- • Ability to translate ambiguous employee friction problems into simple, reliable technical solutions.
- • Strong judgment around security, privacy, least privilege, and safe automation in an enterprise environment.
- • Strong collaboration and communication skills. You will work cross-functionally to plan, design, and implement automation together.
- • Experience with automation or integration platforms (Tines, MuleSoft, Workato, etc.).
- • Experience with MCP (Model Context Protocol) server development.
- • Experience building agents on Databricks (Agent Bricks) or with frameworks such as OpenAI Agents SDK or LangGraph/LangChain.
- • Experience with MLflow tracing/evaluation, vector search, and LLM observability.
- • Experience integrating IT platforms: Okta/Microsoft Entra ID, Google Workspace, Jira, Slack, Jamf, GitHub, Workday, or SaaS administration APIs.
- • Familiarity with identity lifecycle, access management, device management, and software provisioning.
Preferred Skills & Technology Stack
- + Experience with big data tools (Apache Spark, Kafka, Flink) and cloud platforms (AWS, Azure, GCP).
- + Familiarity with dbt (data build tool) for SQL transformations.
- + Hands-on experience with Docker and Linux system administration.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Databricks
Verified EmployerDatabricks champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.