Demonstrating contribution through a Dataverse-first proof project.

Contents

• Coding ability as a hiring signal
• The 2026 entry-level hiring context
• Context& and xPM: public information
• What the Microsoft platform already provides
• Understanding Dataverse beyond the official documentation
• Working method with an AI coding agent
• Proposed proof project
• Setting up a development environment and repository
• 30-day learning and application plan
• The unsolicited application
• Official Microsoft Learn path

Executive summary

Dataverse first. Business and platform understanding as the primary signal.

1. Context& is publicly positioned as an AI-first Microsoft partner working across Power Platform, Azure, Microsoft 365, Dynamics 365 and project portfolio management.
2. Projectum xPM is publicly described as portfolio/strategy management built on Microsoft Power Platform and connected to Teams, Power BI, Azure DevOps, Jira and more.
3. The best signal is a small, documented proof artifact: data model, security model, app, automation, report, screenshots, and known gaps.

Coding ability as a hiring signal.

Code fundamentals remain necessary, but they have become a baseline expectation now that AI tools can generate code quickly. The less common and harder to demonstrate capability is understanding a business problem, selecting an appropriate platform component, and showing that the result fits how an organisation works.

Coding tests (traditional signal)

Useful for assessing fundamentals, but a weaker hiring signal now that AI tools can generate isolated code quickly.

Understanding context (emerging signal)

Evidence of understanding portfolio management, roles, data ownership, permissions, reporting and Microsoft-native workflows.

A working artifact (strongest evidence)

A small Dataverse proof with documented assumptions and evidence is more informative than a CV statement of motivation.

Suggested framing: “I used AI to research and check my work faster, but what I am trying to show is an understanding of the platform and the business, not only the ability to write code.”

The 2026 entry-level hiring context.

The current job market shapes why this approach is proposed. The relevant labour-market findings are summarised below, followed by the rationale for an unsolicited, targeted proof project.

Junior hiring has contracted (entry-level decline)

Junior software postings are down roughly 28% from their 2022 peak and have not recovered, and workers aged 22 to 25 in AI-exposed roles have seen the sharpest decline.

Few people are changing jobs (low mobility)

2026 has been described as a low-hire, low-fire market. With little job movement, few junior roles are posted, so responding to advertised positions competes for a small number of openings.

Targeting a specific employer (implication)

When few roles are posted, hiring tends to occur for specific candidates judged clearly worth an exception. A proof project built for one organisation addresses that case directly, rather than competing for an advertised posting.

On receiving no reply: a lack of response is common in this market and is not necessarily an indication of the work's quality. The artifact is reusable: it can be published, sent to other Microsoft partners, or added to a portfolio. Sources for the figures above: Stanford/ADP research (2025) and the U.S. Bureau of Labor Statistics (2026).

Context& and xPM: public information.

The following summary is drawn entirely from public web pages. It is intended to establish background, not to imply any internal knowledge of the company.

AI-first Microsoft partner (Context&)

Public sources describe Context& as formed from Delegate, Projectum and Consit, working across the Microsoft ecosystem: AI, Dynamics 365, Power Platform, Azure, Microsoft 365, project portfolio management, licensing, cloud strategies and digital transformation.

Connecting strategy to execution (xPM)

Public xPM material describes it as more than traditional project portfolio management. The idea is to link every unit of work, whether a project, an operational process, or a hybrid, directly to an organisation's strategic goals, so the whole organisation's work connects to strategy instead of projects being managed in isolation.

Built on Microsoft Power Platform (platform)

The public xPM material says Projectum xPM is built on Microsoft Power Platform and integrates with tools like Teams, Power BI, DevOps and Jira. That makes Dataverse the thing most worth learning well.

Customers already use Microsoft (buyer context)

A likely customer already has Microsoft 365 / Power Platform capability. The opportunity is to understand what the Microsoft stack already provides and how to extend it safely, rather than rebuilding it.

What the Microsoft platform already provides.

In this ecosystem, Microsoft already provides much of what a developer might otherwise build from scratch. The table below maps a common instinct to the native platform component and what to study instead.

Where to focus: Dataverse data modelling, Dataverse security, model-driven app UX, Web API, solution ALM, and reporting. PCF and custom code matter later; the signal most people are missing is Dataverse-first judgement.

On the choice of niche: generic web-development portfolios are now numerous and harder for recruiters to assess. Dataverse and Microsoft business applications have enterprise demand, fewer candidates, and proofs that are harder to fabricate, so comparable effort is more distinguishable.

Understanding Dataverse beyond the official documentation.

Microsoft Learn explains how Dataverse works. To understand the business case, it helps to read how independent practitioners describe what it is and why organisations build on it. Most of these explainers are written by Microsoft partners who sell related services, so their perspective should be attributed when cited.

What it is, in plain language

A cloud business-data platform (explainer)

“Microsoft Dataverse is a cloud-based, low-code data service and platform that allows organizations to store and manage data used by business applications securely.”

SysKit — Dataverse explained, vs SharePoint Lists & SQL

Built for modern business data (explainer)

“Microsoft Dataverse is a powerful, cloud-based data platform designed to meet the evolving data management needs of modern businesses.”

Pragmatiq — What is Microsoft Dataverse? (2025)

Governed data + low-code logic (explainer)

A managed data service on Azure that keeps business data and low-code logic structured, secure and compliant. A clear Nordic-partner walkthrough written for makers.

Precio Fishbone — What is Microsoft Dataverse?

Why a business builds on it instead of a custom backend

Governance out of the box (Dataverse vs SQL)

“Pick Dataverse when you want governance out of the box… role-based access, controlled sharing, and guardrails that reduce low-code risk.” It is usually faster to set up than building those controls yourself.

Precio Fishbone — Dataverse vs SQL Server · SSW — When to use Dataverse

Faster time-to-value (architect view)

“Dataverse is built for low-code development, app integration, and rapid deployment,” with built-in security, audit trails and workflow automation managed by Microsoft, and no database infrastructure to run yourself.

i3solutions — Dataverse vs SQL Server for architects

When Microsoft's own team chose it (a real decision)

“Dataverse emerged as the clear winner for user-facing applications, thanks to its seamless integration with the Power Platform.” A concrete worked example of the same build-vs-custom choice.

Microsoft Inside Track — Dataverse vs SQL Server

Reported ROI: a Forrester Total Economic Impact study commissioned by Microsoft (July 2024) modelled a composite organisation and reported 224% ROI and $81.7M NPV over three years, driven by roughly 25% time savings on processes built with low-code tools. When citing such figures, note that the study was commissioned by Microsoft. Forrester TEI of Microsoft Power Platform (2024)

Working method with an AI coding agent.

When working with an AI coding agent, code production is no longer the main constraint. The constraints become the quality of the goal and context given to the agent, and the ability to verify and explain the result. The practices below follow from that.

01. State the goal

Before requesting anything, write one or two plain sentences stating the problem, who it is for, and what a good result would be. A vague request produces vague output; a clear goal produces useful output.

02. Define success first

Define the check that proves success before building: a test, a demonstration, or a result that can be pointed to. Setting that criterion in advance measures the work rather than relying on hope.

03. Trust the check, not the confidence

An AI states results with equal confidence whether they are correct or not. The working demonstration, the passing test and the actual output are the evidence, not an assurance that something should work.

04. Provide real context

Give the agent a short brief: the problem, the intended users, and the rules it must respect. More context reduces guessing, which is where errors are introduced.

05. Work in small finished slices

Build pieces that each work fully, rather than one large item that is partly done and difficult to check. Small, complete steps are easier to verify and correct.

06. Record decisions

Keep goals, choices and status in notes or files rather than in a transient chat, so the work can be resumed by anyone, including later by oneself.

07. Require evidence

Look for proof that can be seen: a screenshot, a running demo, a link to the documentation it relied on. “It works” is a claim; a demonstrable result is the standard to hold to.

08. Be able to explain every decision

The agent assists with building but does not replace understanding. A decision that cannot be explained is not yet owned; AI should be used to learn faster, not to mask a lack of domain understanding.

Quality criteria: every assumption is labelled, every platform fact links to official documentation, every build step has a screenshot or a reproducible note, the README explains the business value before the technical detail, and the final message links to evidence rather than promises.

Proposed proof project.

The project should be small enough to complete, but close enough to the xPM domain to demonstrate business understanding.

Portfolio Review Room: a Dataverse-first mini PMO workspace (recommended concept)

A small model-driven app for a portfolio manager to review initiatives, risks, resources, decisions and OKRs before a steering meeting. It is not meant to copy xPM, which is a mature product built over years. The aim is to demonstrate an understanding of the category and the Microsoft platform underneath it.

Tables (data model)

• Portfolio
• Initiative / Project
• OKR / KPI
• Resource request
• Risk / Issue
• Decision log
• Status snapshot

Roles (security)

• Portfolio viewer
• Project manager
• PMO editor
• Executive reader
• Admin / maker

App surfaces (experience)

• Model-driven app forms/views
• Power BI summary report
• Teams tab or Teams-ready screenshot
• Power Automate review digest

Setting up a development environment and repository.

Two prerequisites to resolve early, as both have constraints that can cause delays if encountered late.

Get a free Dataverse environment

University account (option A)

If the university provides a Microsoft 365 / Entra sign-in, the Power Apps Developer Plan can be requested at aka.ms/PowerAppsDevPlan. It is free, requires no credit card, and includes Dataverse, model-driven Power Apps and Power Automate. It either provisions immediately or returns an "administrator has turned this off" message.

A self-created tenant (option B)

If no school account works, create a free Microsoft 365 / Entra tenant and become its administrator, then enable the Developer Plan inside it. This is not subject to tenant restrictions, and remains free with no credit card.

Dynamics 365 trial (option C)

For an immediate start, a personal account can use a Dynamics 365 30-day trial to build on Dataverse, then migrate to Option A or B before the trial expires.

Constraints: the Developer Plan needs a work or school (Entra) account; a personal outlook.com or gmail.com is rejected. The Microsoft 365 Developer Program is not a route here unless a Visual Studio Professional or Enterprise subscription is already held, because its free E5 sandbox now requires one. An Azure free account is not the route to Dataverse; it is only for later extensions such as Azure Functions, and it does require a credit card.

Setting up the development repository

Making the solution diffable (concept)

A Dataverse solution exports as one .zip that git cannot read. The Power Platform CLI (pac) unpacks it into many small files git can track. Install with dotnet tool install --global Microsoft.PowerApps.CLI.Tool or the VS Code Power Platform Tools extension.

Export, unpack, commit (process)

pac auth create to the dev environment, then pac solution export to a .zip, then pac solution unpack --folder ./src. Commit /src. Iterate: customize in the environment, re-export, re-unpack into the same folder, commit again.

README first (layout)

Keep it simple: README.md with the business brief first, /src for the unpacked solution, /docs for the schema diagram, security-role matrix and screenshots, and a .gitignore. Optionally commit a pac solution check report as a quality signal.

Keep out of git: secrets, connection strings, API keys, .env files, the built .zip, and any environment-specific values (parameterize those with environment variables and connection references). Power Platform's built-in Git integration is now generally available but only supports Azure DevOps and needs Managed Environments, so on GitHub use the pac export-and-unpack flow above. The repository built this way is also the context the AI agent reads, so a clear README and a clean /src make the agent considerably more useful.

30-day learning and application plan.

The plan is scoped around exams and limited time. The objective is credible evidence rather than a complete product.

Days 1–3 — Read and map

Read the Context& and xPM public pages. Read Microsoft Learn pages for Dataverse, Power Apps, security roles and Developer Plan. Write a one-page “what I think the business does” brief.

Days 4–8 — Set up environment and repo

Resolve the Developer Plan environment and the development repository from the "Set up" section above early, so that a blocker does not cause delay. Then create the first Dataverse tables and relationships, and keep screenshots from the start.

Days 9–15 — Build the core model-driven app

Create forms, views and sample records for the Portfolio Review Room. Avoid over-designing; keep it quick to understand.

Days 16–20 — Add permissions and automation

Create a security-role matrix. Add a simple Power Automate digest or approval-style flow. Explain why each role exists.

Days 21–25 — Add reporting

Connect Power BI or create a simple dashboard/report view. Focus on useful portfolio questions: risk exposure, overallocated resources, budget trend, stale decisions.

Days 26–30 — Package the proof

Record a short demo, polish README, list known gaps, add official docs links, and send a short unsolicited application that points to the proof artifact.

The unsolicited application.

Rather than requesting consideration, the message gives the reader something concrete to evaluate.

Keep it short (message structure)

1. Who you are: IT university student finishing exams.
2. Why Context&: Microsoft ecosystem, Power Platform, xPM / portfolio management, AI-first work.
3. What you built: link to the proof project.
4. What it demonstrates: Dataverse model, security roles, model-driven app, automation/reporting, documented assumptions.
5. What you want: 20 minutes of feedback or a conversation about where a junior could contribute.

Email seed (draft)

Subject: A Dataverse-first proof project for Context& / xPM (student application)

Hi Context& team,

I am an IT university student currently finishing my exams. Instead of sending only a CV, I wanted to show how I learn a business and a platform.

I researched Context& and Projectum xPM from public sources, then built a small Dataverse-first proof project: a Portfolio Review Room for PMO-style initiative, risk, resource and decision review. It includes a Dataverse data model, model-driven app screenshots, a security-role matrix, a simple automation/reporting idea, Microsoft Learn references, and a list of assumptions and gaps.

The project is not meant to copy xPM. Rather than building generic login and backend code, I focused on learning the Microsoft platform itself (Dataverse, security roles, solutions) and the business workflow on top of it. One thing that surprised me while learning is how much of the identity, data and permission plumbing the platform already handles, so I tried to spend my time on the modelling instead.

If useful, I would appreciate 20 minutes of feedback on whether this is the right direction for a junior profile interested in Power Platform / Dataverse / Microsoft business applications.

Best regards,
[Name]
[LinkedIn]
[Proof project link]

Official Microsoft Learn path.

The official Microsoft docs to read while building. Every link here was checked, and these pages are the source for the platform facts above.

Public Context& / xPM sources

• Context& homepage — Public Microsoft ecosystem and AI-first positioning.
• Context& merger / AI-first news — Public company origin and Microsoft solution areas.
• What is xPM? (Context& blog) — Public definition: xPM as connecting strategy to execution across all work.
• Projectum xPM — Public xPM positioning and portfolio-management value.
• Discover Projectum xPM — Public Power Platform, Teams, Power BI and DevOps integration statements.
• Data management & Analytics — Public Power Hub / analytics add-on description.
• Microsoft Marketplace: Power PPM — Marketplace product summary and capability list.
• Microsoft Marketplace: Power Hub — Marketplace Power Hub summary.