Senior Data and AI Engineer

Intapp•Charlotte, NC

About The Position

Intapp is hiring a senior engineer to build its data foundation and governed AI services. The role owns pipelines from source to model using Python and dbt, deploys workloads with Docker and Kubernetes on AWS, and exposes trusted data to agents through Model Context Protocol (MCP) servers. It is a hands-on position spanning data engineering and applied AI, with direct partnership with business teams to turn requests into useful, reliable solutions. The engineer will design, build, and operate batch and incremental pipelines; containerize and deploy workloads; develop governed MCP servers and connectors; and operate agentic workflows in production. The role also contributes to secure and auditable access patterns, evaluates emerging AI techniques, works with stakeholders to deliver outcomes in visible increments, and reviews code and mentors engineers. Intapp describes a modern, connected yet flexible work environment and emphasizes professional development, employee well-being, collaboration, and community support. The company offers training and continuing-education reimbursement, modern open offices, complimentary lunches, stocked kitchens, wellness programs, flexible time off, benefits for employees and families, family-formation support, paid volunteer time off, and donation matching.

Requirements

At least 8 years of professional data engineering experience, including ownership of production systems end to end.
Strong Python skills for data processing, automation, and services.
Hands-on dbt experience with models, tests, macros, and deployment.
Experience using Docker and Kubernetes to deploy, scale, and troubleshoot data workloads.
Experience with Git, CI/CD, and infrastructure as code.
Solid AWS experience across core storage, compute, networking, and security services.
Advanced SQL skills and a sound understanding of data modeling, warehousing, and performance tuning.
Practical knowledge of MCP, including building, deploying, or integrating MCP servers or similar LLM tool interfaces.
Experience building or deploying agentic workflows, including orchestration, tool calling, evaluation, and monitoring, with sound judgment about useful AI applications.
Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.

Nice To Haves

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

Responsibilities

Design, build, and operate batch and incremental data pipelines in Python, using dbt for transformation, testing, and documentation; resolve issues at the source.
Containerize workloads with Docker and run them on Kubernetes, using infrastructure as code and CI/CD to deploy safely and frequently.
Build on AWS services for storage, compute, orchestration, and security, and tune systems for cost and performance.
Design and build MCP servers and connectors that provide agents with governed access to enterprise data and tools.
Deploy and operate production agentic workflows, including tool use, orchestration, evaluation, observability, and guardrails.
Establish patterns for authentication, access control, and auditability, and evaluate new AI techniques for production use.
Partner with stakeholders to define problems and outcomes, then deliver solutions in small, visible steps.
Review code and mentor engineers, improving standards for design, testing, and documentation.