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L3 Data Technician

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Role Profile


This occupation is found in all sectors where data is generated or processed including but not limited to finance, retail, education, health, media, manufacturing and hospitality.

The broad purpose of the occupation is to source, format and present data securely in a relevant way for analysis using basic methods; to communicate outcomes appropriate to the audience; analyse structured and unstructured data to support business outcomes; blend data from multiple sources as directed and apply legal and ethical principles when manipulating data.

In their daily work, an employee in this occupation interacts with a wide range of stakeholders including colleagues, managers, customers and internal and external suppliers. They would typically work as a member of a team; this may be office based or virtual.

An employee in this occupation will be responsible for collecting and processing data under the guidance of a senior colleague or multiple colleagues across the business. This may vary by sector and size of the organisation. An employee would mainly be responsible for their own work but may have the opportunity to mentor others.

Data Technician Knowledge & Skills Taught:

As with all of our level 3 & 4 apprenticeships, you are not required to have any pre-requisite grades or skills. The purposes is to upskill the individual to the point of industry level competency within the given time period, whilst working within the field. Apprenticeships are a fusion of learning theory and applied experience, to produce applicable competency within a given field.

Competency

  • Source and migrate data from already identied dierent sources
  • Collect, format and save datasets
  • Summarise and explain gathered data
  • Blend data sets from multiple sources and present in format appropriate to the task
  • Manipulate and link dierent data sets as required
  • Use tools and techniques to identify trends and patterns in data
  • Apply basic statistical methods and algorithms to identify trends and patterns in data
  • Apply cross checking techniques for identifying faults and data results for data project requirements
  • Audit data results
  • Demonstrate the dierent ways of communicating meaning from data in line with audience requirements
  • Produce clear and consistent technical documentation using standard organisational templates
  • Store, manage and distribute in compliance with data security standards and legislation
  • Explain data and results to dierent audiences in a way that aids understanding.
  • Review own development needs
  • Keep up to date with developments in technologies, trends and innovation using a range of sources
  • Clean data i.e. remove duplicates, typos, duplicate entries, out of date data, parse data (e.g. format telephone numbers according to a national standard) and test and assess condence in the data and its integrity.
  • Operate as part of a multi-functional team
  • Prioritise within the context of a project

Knowledge

  • Range of dierent types of existing data. Common sources of data - internal, external, open data sets, public and private. Data formats and their importance for analysis. Data architecture - the framework against which data is stored and structured including on premises and cloud.
  • How to access and extract data from a range of already identied sourcesHow to collate and format data in line with industry standards
  • Data formats and their importance for analysis Management and presentation tools to visualise and review the characteristics of data Communication tools and technologies for collaborative working
  • Communication methods, formats and techniques, including: written, verbal, non-verbal, presentation, email, conversation, audience and active listening Range of roles within an organisation, including: customer, manager, client, peer, technical and non-technical
  • The value of data to the business How to undertake blending of data from multiple sources
  • Algorithms, and how they work using a step-by-step solution to a problem, or rules to follow to solve the problem and the potential to use automation
  • How to lter details, focusing on information relevant to the data project
  • Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data
  • The range of common data quality issues that can arise e.g. misclassication, duplicate entries, spelling errors, obsolete data, compliance issues and interpretation/ translation of meaning
  • Dierent methods of validating data and the importance of taking corrective action
  • Communicating the results through basic narrative
  • Legal and regulatory requirements e.g. Data Protection, Data Security, Intellectual Property Rights (IPR), Data sharing, marketing consent, personal data denition. The ethical use of data
  • The signicance of customer issues, problems, business value, brand awareness, cultural awareness/ diversity, accessibility, internal/ external audience, level of technical knowledge and prole in a business context
  • The role of data in the context of of the digital world including the use of eternal trusted open data sets, how data underpins every digital interaction and connectedness across the digital landscape including applications, devises, IoT, customer centricity
  • Dierent learning techniques, learning techniques and the breadth and sources of knowledge

Mindset

  • Manage own time to meet deadlines and manage stakeholder expectations
  • Work independently and take responsibility
  • Use own initiative
  • A thorough and organised approach
  • Work with a range of internal and external customers
  • Value dierence and be sensitive to the needs of others

Typical Job Roles:

  •   Database Technician
  •   Data support analyst
  •   Junior data analyst
  •   Junior information analyst

Apprenticeship Pathways:

These are the courses roadmaps. It is a portion of the work which will follow through the entirity of the apprenticeship course. With on the job experience, and off-the-job learning rounding out in the rest of the training.

Generic - Data Technician Apprenticeship Pathway

Microsoft - Data Technician Apprenticeship Pathway

Data Technician Courses:

All relevant courses within your selected pathway, for yourself or your employee, can be found below.

Data Analysis (Generic Only)

Analysing Data with Power BI (Microsoft Only)

The GKA Way

Python Programming 1 (Microsoft Only)

Introduction to Python and Data Analysis (Generic Only)

Communicating and Storytelling with Data

The EPA Way


Resources

Technical Competencies

Getting Started - Data Technician

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