Senior Data and AI Engineer

Intapp

. Design, build, and operate batch and incremental Python data pipelines .

Posted 10/27/2026full-timeCharlotte • North Carolina • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in designing and operating data pipelines using Python, dbt, and AWS services, while ensuring system performance and cost efficiency. Proficient in containerization with Docker and Kubernetes, along with strong capabilities in CI/CD and infrastructure as code.

Highest-signal resume keywords

Python Data ProcessingDbt ExperienceDocker and KubernetesAWS ServicesAdvanced SQL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModelingPerformance TuningMCP KnowledgeAgentic WorkflowsAutomationTestingDocumentationInfrastructure as CodeCI/CD

Soft Skills

MentoringStakeholder CollaborationProblem Definition

Tools & Technologies

DockerKubernetesAWSGitDbtAirflowRedshiftSnowflakeBigQueryLLM Evaluation Frameworks

Certifications & Qualifications

Bachelor's Degree in Computer ScienceEngineering Degree

Industry Keywords

Data WarehousingOrchestrationAutomationAccess ControlAuditabilityCRM SystemsERP SystemsFinance Source Systems

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSBigQueryDockerERPKubernetesPythonSnowflakeSQL

About the role

Key responsibilities & impact

Design, build, and operate batch and incremental Python data pipelines
Use dbt for transformation, testing, and documentation, and fix issues at the source
Containerize workloads with Docker and run them on Kubernetes
Use infrastructure as code and CI/CD to ship safely and frequently
Build on AWS services for storage, compute, orchestration, and security
Tune systems for cost and performance
Design and build MCP servers and connectors for governed agent access to enterprise data and tools
Deploy and operate agentic workflows in production, including tool use, orchestration, evaluation, observability, and guardrails
Establish authentication, access control, and auditability patterns
Evaluate new AI techniques and move valuable ones into production
Partner with stakeholders to define problems, agree on outcomes, and deliver incrementally
Review code and mentor engineers
Raise standards for design, testing, and documentation

Requirements

What you’ll need

8 or more years of professional data engineering experience
Experience owning production systems end to end
Strong Python for data processing, automation, and services
Hands-on dbt experience with models, tests, macros, and deployment
Working experience with Docker and Kubernetes
Experience with Git, CI/CD, and infrastructure as code
Solid AWS experience across storage, compute, networking, and security
Advanced SQL
Knowledge of data modeling, warehousing, and performance tuning
Practical knowledge of Model Context Protocol (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
Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience
Nice to have: Airflow or comparable orchestration experience
Nice to have: Experience with Redshift, Snowflake, or BigQuery
Nice to have: Familiarity with CRM, ERP, and finance source systems
Nice to have: Experience with retrieval-augmented generation, vector search, or LLM evaluation frameworks

Benefits

Comp & perks

Professional development opportunities
Reimbursement for training and continuing education
Complimentary lunches
Fully stocked kitchens
Comprehensive wellness programs
Flexible time off programs
Family-formation benefits
Paid volunteer time off
Donation matching program
Modern, open offices
Flexible work environment