Business Data Analyst

Salary: £60,000 per year

Hybrid – 3 days in office

Salary up to £60,000

We need a Business Data Analyst that is passionate about data and able to use various methods to transform raw data into useful data systems. The primary role of the Business Data Analyst is to combine expertise, programming skill, data science and business intelligence to extract meaningful insights from the data.

This is a great opportunity has arisen for a Business Data Analyst, to work within a fast-growing technology company.

The role is hybrid, and the office is in Northamptonshire.

Your skills and experience

as a Business Data Analyst or in a similar role (for example Business Analyst, Analytics Engineer, Data Analyst or BI Developer).

Key skills:

• Strong analytic skills related to working with structured and unstructured datasets.
• Highly organised critical thinker with a great attention to detail.
• Exceptional communication and presentation skills in order to explain your work to people who don’t understand the technical details.
• Effective listening skills in order to understand the requirements of the business.
• Strong problem-solving with an emphasis on product development, with the ability to come up with imaginative solutions.
• Drive and the resilience to try new ideas if the first one doesn’t work – you’ll be expected to work with minimal supervision, so it’s important that you’re able to motivate yourself.
• Collaborative approach and a ‘go-getting’ attitude, sharing ideas and finding solutions.
• Accountable for the outcome, seeks opportunities and removes obstacles.
• Strong planning, time management and organisational skills.
• The ability to deliver under pressure and to tight deadlines.
• A drive to learn and master new technologies and techniques.

Experience in and knowledge of:

• Data warehousing and working with and creating data architectures.
• Building and optimizing ‘big data’ data pipelines, architectures and data sets.
• Manipulating, processing and extracting value from large, disconnected datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• Data models, data mining, and segmentation techniques.
• Using computer languages to manipulate data and draw insights from large data sets.
• Developing and maintaining ETL/ELT routines.
• BI tools.
• A variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.