Finance Assistant

Worcester
10 months ago
Applications closed

Related Jobs

View all jobs

Finance Data Analyst

Billing Analyst for Telecoms Invoicing & Data Integrity

Trainee Data Analyst/Support – Training Course

Financial Data analyst

Data Analyst, hireful

Data Analysts ITSM and Azure Data Platform

We are seeking a finance assistant/data entry specialist to support the implementation of a new ERP system. The successful candidate will play a key role in migrating data from existing systems into the new ERP, ensuring accuracy, consistency, and completeness of all data. This is a critical position in the project, requiring a high level of focus and precision during the systems implementation process.

Key Responsibilities:
Data Migration: Enter and validate data from legacy systems into the new ERP system, ensuring accuracy, consistency, and completeness.

Data Cleansing: Review and clean existing data to identify and rectify any discrepancies or errors before migration into the new system.

Data Quality Assurance: Conduct thorough checks and validation of migrated data, identifying any gaps or issues, and working closely with the project team to resolve them.

System Testing: Assist in testing the new ERP system by entering test data, monitoring outputs, and reporting issues or errors encountered.

Documentation: Maintain accurate documentation of data entry procedures, including data mapping and transformation rules for future reference.

Collaboration: Work closely with IT teams, business analysts, and other stakeholders to ensure smooth integration of data into the ERP system.

Data Formatting: Format data according to the ERP system requirements and data standards.

Reporting: Generate reports from the new ERP system for analysis, ensuring the completeness and accuracy of all entered data.

Support: Provide ongoing support during the post-implementation phase, assisting with data-related queries and issues.

Experience in finance is essential and any experience in data entry, data migration, or working with ERP systems (particularly during implementations) is preferred.

This is a temporary role, ongoing for a few months and is based onsite in WR2 5 days a week

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.

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.

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.