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About This Role
Description
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Principal Data \& AI Architect, Global D360 (Data Cloud)
The Global D360 Data \& AI Architects team is an innovative group of sellers and GTM specialists at the heart of Salesforce’s newest innovation \- D360\. We are a startup within an extensive organization focused on next\-generation technology at Salesforce. This dedicated team helps Salesforce customers and prospects develop and implement strategies to take their Customer Experience efforts to a new level with Data \+ AI \+ CRM \+ Trust. This team is at the center of our GTM strategy connecting the dots between the D360 Product team, Product Marketing, Enablement, Customer Success \& Support, and Partners Ecosystem to drive growth for D360\. It is a dynamic, constantly evolving environment where expertise in design, and technology is demonstrated every day to drive innovation.
Role Description
The D360 Technical Architect plays a pivotal role in developing innovative solutions for our customers across a variety of industries. You will work closely with Account Executives, Solutions Engineering, Product, and Product Marketing teams to provide deep technical domain expertise during the pre and post\-sale process.
Our D360 customers are only as successful as the value they derive from the platform. You will play a key role in ensuring D360 is the right fit, help them shape and prioritize the most valuable D360 use cases, provide technical architecture best practices, and cost estimations and consumption plans to drive product adoption and success.
You will collaborate with the broader global D360 team to develop use cases, demos, sales plays, architectural plans and fit, and technical thought leadership and POVs. You will also have a meaningful role in driving our Product Roadmap forward as well as serve as a key SME of one or more product pillars, while sharing back innovative ideas and feedback from customers.
It is important to have a solid technical grasp of the CRM, Modern Data Stacks, Analytics \& BI, CRM, AI (Generative and Predictive) and Agentic Enterprise landscape, and the ability to effectively communicate our offerings to potential clients both business and technical personas.
Key Responsibilities
- Solve Business Problems \- Analyze complex business problems by conducting research and assessments to define the problem, generate innovative ideas, see opportunities, and recommend actionable solutions.
- Drive Innovation \& Customer Adoption \- Bring structure to the client's decision\-making process by communicating and evaluating solution options, and facilitating agreement among key stakeholders that helps customers prioritize high\-value solutions, driving business impact.
- Connect the “Art of the Possible” \- Assist Solutions Engineers with delivering software demonstrations, rapid prototyping, and storytelling to show how connected experiences come to life with the Salesforce D360, Agentforce \& Salesforce CRM.
- Cross Platform Collaboration \- You will use your understanding of customers’ use cases across industries and multiple technology landscapes (CRM, Modern Data Stack, Analytics \& BI, CRM and AI, Agents) to develop solutions across the Salesforce's technology stack.
- Provide Technical Domain Expertise \- Answer in\-depth D360 questions related to data governance, security, and other technical capabilities. Create architectural diagrams, write technical thought\-leadership pieces (blogs, whitepapers, etc.), documentation, enablement materials to help us stay ahead of industry trends and help our customers implement best practices with D360\.
- Drive adoption through mastering sizing estimations and mapping to consumption use case plans, working with pre and post sales teams to drive realized usage and success.
- Acts as the "Voice of the Customer" through strategic alignment with the product organization, regularly capturing and championing customer feedback and translating field experiences into actionable product evolution.
Key Requirements
- Experience in solutions engineering/solutions architecture/technical consulting, ideally in the B2B SaaS space, particularly cloud data platforms
- Strong verbal and presentation abilities, capable of effectively communicating ideas to clients and prospective clients at all levels of an organization
- Understanding \& ability to articulate the relationship between Data, AI and Customer Relationship Management, aka the Customer360
- Demonstrable ability to shift clients to alternative solutions when initial solutions are not a fit, with examples to support this. Demonstrable experience leading strategy and digital roadmap projects in a complex business environment
- Experience with Data Warehouses, Data Lakes, Lakehouses, Agentic AI, Cloud (Hyperscalers) Technology, Business Intelligence and CRM products
- Experience in programming languages such as Javascript, Python, and SQL or Salesforce App Development with LWCs, Apex, Flow etc
Preferred Requirements
- Implementation or Sales Experience in Salesforce D360 and Agentforce
- Hands on Experience on Salesforce CRM technology like Sales Cloud, Service Cloud, Marketing Cloud or any Industry Clouds
- Broad range of experience in large\-scale database and data warehousing technology, like Snowflake or Databricks, as well as ETL processes, analytics and cloud technologies, Data Engineering, Data Science
- Hands on Experience in AI/ML solutions like Einstein, Sagemaker, and Vertex. Solid Understanding of Generative AI
- Hands on Experience designing data solutions on cloud platforms like Amazon Web Services, Microsoft Azure or Google Cloud Platform
- Hands\-on expertise with analytics tools like Tableau, PowerBI, Looker, etc as well as Data Governance and MDM (ex. Informatica)
- Familiarity with data activation or "reverse ETL" platforms in the context of Composable Data Platforms (CDP) and integrating various marketing technologies and data tools into a cohesive system
- Experience will be evaluated based on alignment to the core competencies for the role (e.g. extracurricular leadership roles, military experience, volunteer work, etc.)
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At Informatica, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $292,900 based on 108 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Informatica AI Hiring
Informatica has 39 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Architect, AI Software Engineer. Positions span IN, US, CA, US, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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