Introduction
At IBM Canada, we believe AI without trusted data is just hype. Our Data Platform Technical Sales team works at the intersection of enterprise data strategy and real client outcomes — helping Canada’s largest organizations move from fragmented data estates to intelligent, governed platforms that actually power their AI ambitions. This isn’t a role for someone who wants to read from a slide deck. You’ll be the person in the room who earns credibility fast — someone who can go deep on architecture with a data engineer in the morning, then reframe the same conversation around business risk and competitive advantage for a CTO in the afternoon. You’ll be part of a team covering IBM’s Data Fabric, Data Security, and Database portfolio — alongside Confluent’s real‑time data streaming capabilities — working directly with enterprise clients across Canada to validate, demonstrate, and accelerate their confidence in IBM technology.
Your Role and Responsibilities
As a Customer Success Engineer on the Data & AI Platform team, your primary motion is pre‑sales technical validation and trust‑building — you are the reason a client moves from "interesting" to "I believe this works for us." You’ll partner directly with IBM enterprise sellers, owning the technical narrative in deals and building lasting credibility with client architects, data leaders, and technology executives. Your responsibilities include:
* Own the technical story: design, build and deliver compelling, tailored demonstrations of IBM’s Data Fabric, Data Security, and Database portfolio that connect platform capabilities to each client’s specific architecture and business outcomes.
* Lead proof engagements: run structured POCs, workshops, and solution design sessions that de‑risk client decisions and accelerate deal progression.
* Build trusted relationships: become a go‑to technical resource for your client contacts — not just during the sales cycle, but through onboarding and early adoption.
* Drive thought leadership: contribute to internal enablement, client‑facing content, and team knowledge.
* Translate architecture into outcomes: bridge the gap between technical capability and business value.
Preferred Education
Master’s Degree
Required Technical And Professional Expertise
* 3–5 years of hands‑on experience in data engineering, data architecture, or a technical consulting role.
* A working understanding of enterprise data architecture patterns — data fabric, data mesh, lakehouse, streaming, and governance.
* Some exposure to client-facing or stakeholder communication.
* The ability to tell a story.
* Intellectual curiosity and a bias for learning.
* Comfortable working in a dynamic, deal‑driven environment where priorities shift and timelines are real.
Preferred Technical And Professional Experience
* Data Fabric & Integration: practical knowledge of data fabric concepts and integration architecture.
* Data Security & Governance: understanding of enterprise data security principles.
* Database platforms: hands‑on experience with one or more enterprise database technologies.
* Real‑time & streaming data: awareness of event‑driven architecture and streaming platforms.
* Modern data stack fluency: comfort discussing technologies like Apache Iceberg, Datalake, Db2, Spark, or similar.
* Demonstration readiness: the ability to build and deliver a credible technical demo from scratch.
* Great communication skills to articulate complex technical solutions and to tie them to business values.
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