Tebra
Healthcare
AIAutomationEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Automation Engineer at Tebra. Skills: Workato, AI Automation, Data Pipelines, Enterprise Integrations. Design, build, and optimize integrations, automations, and AI-driven workflows. Own the Workato automation stack”
What You'll Achieve.
measurable improvements in data accessibility; system efficiency; operational performance; reduce manual work; accelerate insights
Industry & Context.
problem-solving; critical thinking
What They're Looking For.
Must Have
2-5 years of experience in integration, automation, AI, software or data engineering, 1+ year hands-on in Workato, Proven expertise with Enterprise iPaaS required (Workato, Mulesoft, or similar), Proficiency in designing and implementing integrations across Salesforce, NetSuite, Slack, and Snowflake, Hands-on technical background in ETL/ELT, Reverse-ETL, data modeling, SQL/T-SQL, and programming (Python, Ruby, or similar), Experience using vector databases such as Pinecone, Snowflake Cortex Search and pgvector, Experience with AI/ML concepts and implementing AI workflows in enterprise environments, Hands-on experience leveraging APIs and programming languages to build and maintain scalable enterprise integrations, Experience with GitHub for version control, Jira for work tracking, and Confluence/Lucid for documentation, problem-solving, critical thinking, and ability to execute under minimal supervision, Proficiency with Snowflake, Salesforce, and NetSuite, Track record of introducing innovation in automation and AI while maintaining governance and system reliability
Nice to Have
Experience with Cloud Platforms such as GCP or AWS preferred, architectural and automation design skills preferred, Workato Automation Pro or SnowPro certifications preferred
What You'll Do.
and optimize integrations
and AI-driven workflows
Own the Workato automation stack
Develop resilient data pipelines
Implement AI-enabled capabilities
Translate business needs into scalable
and maintain Workato recipes
Implement error handling
and reusable design patterns
Architect multi-agent systems
Partner with stakeholders to design corporate systems
fallbacks and error recovery
Develop and automate ELT/ETL processes
Build evaluation into every pipeline
Own retrieval end-to-end
Implement data enrichment
and architectural safeguards
Production observability
Document agent behavior
decision logic and failure modes
A testing agent versions
Apply data governance practices
Maintain technical documentation
How You'll Work.
Team & Collaboration
Collaborate directly with stakeholders to translate business needs into scalable, automated solutions
Full Job Description
Tebra only initiates contact with candidates via email from an official Tebra email address (@tebra.com, @patientpop.com, or @kareo.com) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging. About the Role We are seeking a hands-on AI & Automation Engineer to design, build, and optimize integrations, automations, and AI-driven workflows that power our enterprise data and business operations. You’ll own the Workato automation stack, develop resilient data pipelines between core platforms (Salesforce, NetSuite, Snowflake, Slack), and implement AI-enabled capabilities that reduce manual work and accelerate insights. This role requires deep technical execution skills in integration development, data modeling, and programming, combined with a practical understanding of AI workflows. You will collaborate directly with stakeholders to translate business needs into scalable, automated solutions — delivering measurable improvements in data accessibility, system efficiency, and operational performance. Your Area of Focus Integration & Automation Development Design, build, and maintain Workato recipes, connectors, and orchestrations for Salesforce, NetSuite, Slack, and Snowflake. Implement error handling, observability, and reusable design patterns to ensure reliability and scalability. Agentic System Design Architect multi-agent systems: tool selection, planning loops, state management, human-in-the-loop Checkpoints. Partner with stakeholders to design corporate systems (Salesforce, NetSuite, Snowflake, Slack, etc) as agent-callable tools, not just data sources. Build guardrails, fallbacks and error recovery for non-deterministic workflows. Data/RAG Pipeline Design & Management Develop and automate ELT/ETL processes to support both BI Analytics and AI retrieval across similar datasets
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