Senior Data and AI Engineer

IntApp builds governed data and AI services that teams can trust. As a Senior Data and AI Engineer, you will design and operate end-to-end data pipelines and deploy production AI workflows, with modern tooling across Python, dbt, containers, and AWS. The role is onsite in Charlotte, NC.

What you can expect at IntApp

• Wellness and flexibility: comprehensive wellness programs plus flexible time off.
• Support for family planning: family-formation benefits and support.
• Grow your skills: reimbursement for training and continuing education, supported by a community of experienced professionals.
• Keep things comfortable: complimentary lunches and fully stocked kitchens in modern, open offices.
• Give back: paid volunteer time off and a donation matching program.

Responsibilities

• Design, build, and operate batch and incremental pipelines using Python, with dbt for transformation, testing, and documentation; troubleshoot and fix issues at the source.
• Containerize workloads with Docker and run them on Kubernetes, using infrastructure as code and CI/CD to deliver safely and frequently.
• Build solutions on AWS across storage, compute, orchestration, and security, tuning for cost and performance.
• Design and build MCP servers and connectors to provide agents governed access to enterprise data and tools.
• Deploy and operate agentic workflows in production, including tool use, orchestration, evaluation, observability, and guardrails.
• Establish patterns for authentication, access control, and auditability, and test new AI techniques so only effective approaches reach production.
• Partner with stakeholders to define the problem, align on outcomes, and deliver improvements in small, visible steps.
• Review code and mentor engineers, raising standards for design, testing, and documentation.

Requirements

• 8+ years of professional data engineering experience, including owning production systems end to end.
• Strong Python for data processing, automation, and services, plus hands-on dbt (models, tests, macros, deployment).
• Hands-on experience with Docker and Kubernetes to deploy, scale, and troubleshoot data workloads, with Git, CI/CD, and infrastructure as code.
• Solid AWS experience covering storage, compute, networking, and security.
• Advanced SQL, with a strong grasp of data modeling, warehousing, and performance tuning.
• Practical knowledge of Model Context Protocol (MCP), including building, deploying, or integrating MCP servers or similar tool interfaces for LLMs.
• Experience building or deploying agentic workflows (orchestration, tool calling, evaluation, monitoring) and the judgment to distinguish useful patterns from hype.
• Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.

Nice to have

• Orchestration experience with Airflow or a comparable tool.
• Experience with cloud data warehouses such as Redshift, Snowflake, or BigQuery.
• Familiarity with enterprise source systems such as CRM, ERP, and finance platforms and their data quirks.
• Experience with retrieval-augmented generation, vector search, or LLM evaluation frameworks.

Technologies you may work with: Python, dbt, Docker, Kubernetes, AWS, Git, CI/CD, infrastructure as code, SQL, Model Context Protocol (MCP), LLMs, Airflow, Redshift, Snowflake, BigQuery.