Lead Technical Consultant

Lead Technical Consultants carry the technical seat on a delivery team. You set the standard others build to, review and disposition the team's generated work, and own the technical conversation with the client on the engagements you lead. Perficient's Salesforce practice delivers in small, AI-first teams. AI drafts most of what used to be written by hand — requirements, stories, configuration, tests and documentation — and a named person reviews, corrects and approves every piece of it, and is accountable for it. We hire people who can direct that work, defend it in front of a client, and keep learning as the tools change.

What you'll do 

The pillars of being a successful Lead Technical Consultant at Perficient are:

• Own the technical design for a pod's scope: data model, security, integration approach and build order.
• Set the technical standards, naming conventions and branching strategy before build begins, and enforce them.
• Review the team's generated code and configuration and disposition every finding as confirmed, corrected or escalated. You are accountable for what a first-pass review missed.
• Record technical approval on every story before it enters a sprint, overriding drafted technical assessments where they are wrong.
• Verify platform capability claims against current documentation before they reach a client or a design. Models state current, deprecated and invented facts with equal confidence.
• Build hands-on, especially the hardest pieces, and stay close enough to the code to judge it.
• Own deployments for your scope, with a rehearsed runbook and a passed smoke test.
• Mentor consultants on building with AI and on reviewing what it produces.

On Marketing Cloud Engagement 

• Build in Marketing Cloud Engagement: journeys, automations, data extensions, content and the contact model.
• Write and maintain AMPscript and server-side JavaScript where the solution needs it.
• Integrate Marketing Cloud Engagement with Salesforce Core and the program's data sources.
• Validate segments and sends before they run — a wrong segment returns a population and nothing errors.
• Support the move toward Marketing Cloud Next and Data 360, and learn them as the work arrives.
• Write tests from the acceptance criteria and verify every journey path in the environment.

How you'll use AI 

• Configure the tooling for the team — repository context, standards files and instructions — so generation produces work to the engagement's standard.
• Use AI for first-pass review across the team's output, then disposition every finding yourself.
• Generate journey logic, AMPscript, queries and test data with AI, and verify each against real data before it runs.
• Use AI to document the existing Marketing Cloud configuration before you change it.
• Review generated work critically before it moves. Generated work fails plausibly — complete, well-formed, internally consistent and wrong — and you are the one who catches it.
• Answer one question about anything you approve: what did you change or correct before you approved it? A reviewer who never has an answer is not reviewing. Work fluently in whatever the engagement runs on — Claude, Microsoft Copilot, GitHub Copilot, platform assistants, or the client's own stack. The activity is mandatory; the tool usually is not.