Lead data Migration Consultant

Manchester
10 months ago
Applications closed

Related Jobs

View all jobs

Enterprise Data Architect - Oracle Fusion

Principal Data Architect

Senior Data Engineer

Data Architect - Mainframe Migration & Modernization

Lead Data Engineer (SC cleared)

Data Architect – Mainframe Migration & Modernization

Are you experienced in Microsoft Dynamics 365?
 
Have you led data migration projects?
 
Are you looking for a role with a great work-life balance and interesting work?
 
This could be the role for you!

Futures are currently supporting a leading manufacturing company as they transition from Microsoft Dynamics AX to a customized Dynamics 365 Finance and Operations ERP solution. We are seeking a seasoned Data professional with a proven track record in Data Migration and Data Architecture. This is a temporary, fixed-term opportunity estimated to last 12 months.

Data Migration Consultant- Role Overview- D365, Dynamics 365, Data Migration, Data

You will support a leading manufacturer whose products are sold Globely. Working alongside their chosen Microsoft partner and in-house Project Management and Development team, you will take ownership of the Data Migration aspect of this ERP transition.

Data Migration Consultant- Ideal Candidate- D365, Dynamics 365, Data Migration, Data

Proven experience in migrating from Dynamics AX to D365 F&O, ideally on multiple occasions.
Background in the manufacturing or FMCG industries, with an understanding of their unique cultures and challenges.
Methodical and process-oriented, with a keen eye for quality and detail.
Team player with excellent stakeholder management skills.
Data Migration Consultant- Key Responsibilities- D365, Dynamics 365, Data Migration, Data

Collaborate closely with the D365 project manager, team members, and subject matter experts across the business.
Lead and serve as the expert for Data and Migration activities to ensure a successful transition from AX to D365 F&O.
Facilitate workshops to understand requirements and develop data migration solutions.
Manage end-to-end data migration activities for the project duration.
Data Migration Consultant- Key Skills- D365, Dynamics 365, Data Migration, Data

Successful migration experience from AX2012 to Dynamics 365 F&O.
Passion for data and data migration.
Extensive use and knowledge of Microsoft data applications stack.
In-depth knowledge of AX, D365, Azure, SQL server, and queries.
Strong understanding of leading databases and design best practices.
Excellent requirements gathering skills, delivering both current and future-state solutions.
Self-motivated with critical thinking, analysis, and problem-solving abilities.
Solid communication and interpersonal skills, enabling effective collaboration.
Ability to set and meet tight deadlines efficiently.
5+ years of AX/D365 data knowledge and migration experience.
Advantageous: 5 years of experience in large enterprises (£60M or greater).
Ability to commute regularly to Rochdale
Does this sound like you? Apply now for more information

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 Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.