About the role
This is a Data 360 platform engineering role. You build the data foundation the program's marketing and AI work depends on — ingestion, modeling, identity and activation — including headless use of Data 360 by other systems and agents. 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. About the engagement You will join a long-term, AI-first Salesforce program for a Fortune 50 global automotive manufacturer, as part of a delivery team that spans India and the US. The program runs across Salesforce Core, Marketing Cloud, Data 360 and Agentforce, and the work moves between them. We are hiring people who learn new systems quickly and go where the work is — on a long engagement that matters as much as what you know today.

What you'll do
▪ Build data streams and map source data to the Data 360 data model. ▪ Configure identity resolution and validate the unified profiles it produces. ▪ Build calculated insights, segments and activations, and verify the populations they return. ▪ Expose Data 360 to other systems and to AI agents through APIs and data graphs — the headless pattern. ▪ Reconcile data both ways against the source, and spot-check records by hand. ▪ Keep consumption cost visible: ingestion volume, refresh frequency and processing drive what the design costs to run. How you'll use AI ▪ Generate mappings, transformation logic and validation queries with AI,



and read every query before trusting the number it returns. ▪ Use AI to profile source data quickly — and check what it summarizes against the data itself. ▪ 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. Qualifications Required ▪ 4–6 years of relevant experience, including client-facing consulting ▪ Data 360 (Data Cloud) implementation — data streams, data model mapping, identity resolution and segmentation ▪ Data modeling and SQL ▪ Data Cloud Consultant certification ▪ Demonstrated, current use of AI tools in delivery work ▪ Strong written and spoken communication, and the ability to explain complex topics to technical and non-technical audiences Preferred ▪ Experience with external data platforms such as Snowflake or Databricks ▪ Marketing Cloud activation experience ▪ Salesforce Platform Administrator certification Who we're looking for Technical depth gets you in the door. Beyond that,



we hire for four qualities together, not any one in isolation — and we want people who are humble enough to teach as readily as they learn, hungry enough to close a gap without being asked, and smart about people as well as platforms. ▪ Techno-functional depth — You understand the business process behind the story well enough to notice when a technically correct build solves the wrong problem. On this program: the client expects excellence in every discipline. Pair technical depth with data-driven reasoning — know your solution, know the evidence behind it, and be ready to defend both. ▪ Client presence — You lead demos and UAT sessions, and you can hold a technical conversation with a client in support of the architect. On this program: the client values radical transparency and accountability without fear. Being in the room means saying the hard thing early, on camera or in person. ▪ AI-native delivery — Generation is your first move, and you can show where you overrode what the tool produced. On this program: the client reviews performance metrics consistently. AI-native delivery here means measurable, defensible output, not only speed. ▪ Learn-fast orientation — You have picked up a recent cloud, language or tool on a live engagement and been productive in it quickly. On this program: raise what you don't know immediately, not after two weeks of pretending otherwise. Gaps surfaced early are welcome; gaps hidden are not. You are already using AI tools to work faster and deliver better outcomes, and you want to keep pushing on that. This is where the practice is headed, so it is a practical, non-negotiable skill, not a nice-to-have.

📌 Data Engineer, Data 360 (Chennai)
🏢 SynapseWorks AI
📍 Chennai