Data Analyst

Alphadog Recruit
Sheffield
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Please note that these qualifications or experience within Data Migrations of D365 are essential for this role, as stipulated by our client.


Applications that do not meet these criteria will not receive a response.


Data Migration Technical Analyst D365


Our client is a small yet growing data migration consultancy that is currently a D365 Data Migration Technical Analysts due to an influx of new contracts.

Prior experience working within in data migration team and in particular experience of migrating D365 is essential.

The company is committed to providing training to enhance your core skills, enabling your growth into the role of a Data Migration Technical Analyst.

As a Data Migration Technical Analyst, you will be expected to possess the technical capability to extract, transform, and load data using predefined methodologies and tools. You will have the opportunity to work alongside our highly experienced consultants, gaining valuable insights into data migration practices and various ERP systems. In addition to your technical expertise, strong analytical skills are essential, as you will need to engage with clients to identify data cleansing tasks and establish field mapping rules. Familiarity with general business processes and system data structures will significantly benefit the core data migration team.

Main Responsibilities

  • Attain comprehensive understanding of the project's data scope, focusing specifically on the detailed scope of your assigned data process area.
  • Develop extraction processes from legacy systems to enable access to raw legacy data.
  • Consult with clients regarding known data issues.
  • Perform data analysis to pinpoint gaps and potential issues, quantifying these alongside known data concerns.
  • Create Data Quality reporting tools aligned with company standards to help users identify and rectify legacy data problems.
  • Collaborate with external System Implementation Consultants to understand how data is imported into the new system, including dependencies or constraints and linkage to system configuration.
  • Conduct mapping sessions with clients to determine how legacy data needs transformation for integration into the new system.
  • Document data issues, gaps, mappings, required business rules, and risks according to company standards, validating this documentation with stakeholders and key users.
  • Develop T-SQL transformation code that meets company standards.
  • Export target data in the required format for loading into the new system.
  • Import transformed data into the new system, resolving any loading errors.
  • Approve the export of newly imported data, ensuring it meets expectations.
  • Investigate and resolve any reconciliation issues or testing bugs related to the data.
  • Provide data extracts as necessary to assist users with reconciliation and testing tasks.

Skills and Experience

Essential Skills, Technologies & Experience:

  • Proficiency in Microsoft SQL Server (2017 and later)
  • Experience of D365 migrations
  • Strong knowledge of SSMS
  • Experience with relational database DDL and DML
  • Advanced T-SQL programming skills
  • Familiarity with T-SQL performance tuning
  • Excellent client-facing communication skills
  • Strong analytical mindset with meticulous attention to detail
  • Commitment to coding and documentation standards
  • Ability to work independently while meeting strict deadlines

Desirable Skills & Technologies (not essential):

  • Exposure to ERP-level or similar data migration projects
  • Familiarity with SSIS (SQL Server Integration Services)
  • Proficient in MS Visual Studio
  • Competence in MS Office, particularly Excel, Word, and SharePoint
  • Understanding of common business system areas (e.g., Finance, Supply Chain, CRM, HRMS, Marketing Systems)
  • Experience in technical team leadership
  • Report building skills, e.g., PowerBI

General Competency

The ideal candidate will be versatile, equally adept at technical work and client-facing analysis. They should rapidly comprehend problems and propose practical alternatives, assessing benefits to recommend solutions effectively. A sense of ownership over their work and a proactive attitude in tackling challenges are crucial. Experience collaborating with business stakeholders to set priorities and resolve issues, along with a comprehensive understanding of IT's role in client business processes, is essential. The candidate should excel in a dynamic environment, take pride in their work, and strive for excellence.

Although the role is remote there will be a need to travel to Reading 3 to 4 times a year for team meetings, normally followed by a social event the next day.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.