Senior Microsoft Dynamics 365 CRM Developer

London
10 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Product Owner, Technology, Data Bricks, Microsoft

Senior Data Engineer (Microsoft Fabric)

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Microsoft Dynamics 365 CRM Developer

Location: Hybrid role 2-3 days in office per week - you can pick one of these base locations London or Birmingham
Employment Type: Full Time / Permanent
Salary: £60k - £90k + Generous Package

Our client is a multi-award-winning Microsoft partner committed to solving real-world challenges with cutting-edge technology. At our Microsoft Business Group, you'll be part of a cloud-native, collaborative environment, delivering impactful results for clients across diverse industries.

As a Senior Microsoft Dynamics 365 CRM Developer, you'll lead the design and implementation of Dynamics 365 CRM solutions for our public sector clients. Collaborating closely with cross-functional teams, you'll contribute to every stage of the solution lifecycle from project planning to ongoing support.

Key Responsibilities:

Analyse client business needs and deliver Dynamics 365 CRM solutions that add value.
Plan and execute CRM implementations while providing regular updates to stakeholders.
Manage Dynamics 365 solutions, ensuring optimal performance and scalability.
Develop and support Power Platform applications, including Power Apps Portals, Model-Driven/Canvas Applications, and Power Automate.
Handle data integrations, migrations, and performance tuning for D365 systems.Essential Skills & Experience:

Strong expertise in Dynamics 365 CRM capabilities, extensibilities, and solution design.
Experience in Power Platform development (Power Apps, Dataverse, Power Automate).
Proficiency in .NET Framework (4.5+), Web API, SQL Server (SSRS, FetchXML).
Hands-on experience with C#, JavaScript, HTML, CSS, JSON, XML, and SQL.Desirable Skills & Experience:

Knowledge of Microsoft Azure.
Experience with data integration and migration projects.
Familiarity with Power BI Reporting.We offer more than just a job, we offer a platform for growth.

A hugely collaborative environment where knowledge sharing is encouraged.
Access to award-winning learning & development programs.
Opportunities to work in an international and diverse team.
The chance to be part of a cloud-first organisation with official partnerships with Microsoft, Databricks, and GitHub.

Apply today and be part of a team that's changing the way businesses embrace technology.

Deerfoot Recruitment Solutions Ltd is a leading independent tech recruitment consultancy in the UK. For every CV sent to clients, we donate £1 to The Born Free Foundation. We are a Climate Action Workforce in partnership with Ecologi. If this role isn't right for you, explore our referral reward program with payouts at interview and placement milestones. Visit our website for details. Deerfoot Recruitment Solutions Ltd is acting as an Employment Agency in relation to this vacancy

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.