Using SFIA to improve Big data/Data science job descriptions.

SFIA provides a common language of skills and skill levels. It is very flexible and this enables organisations to design their own team structures, roles and job titles. They can then select the appropriate configuration of SFIA skills and competency levels to match.

Data science jobs and skills profiles

As in most disciplines - the industry uses job titles as a shorthand reference to jobs. So in organisation structures and job ads we see a variety of titles. e.g. 

  • Chief Data Officer
  • Head of Data Engineering
  • Data Scientist
  • Data Architect
  • Data Engineer
  • Data Integration Architect
  • Data Ingestion Engineer
  • Data Wrangler
  • Business Intelligence (BI) Analyst
  • Data Modeller
  • Data Administrator
  • Analytics report developer

We can add to these the common addition of prefixes used to support job grading and/or career paths. e.g. for government-employed data scientists

  • Head of Data Science
  • Lead Data Scientist
  • Principal Data Scientist
  • Senior Data Scientist
  • Data Scientist
  • Junior Data Scientist
  • Trainee Data Scientist
There are also other positions where data management responsibilities and skills are important - but they only form a part of the overall job. e.g. 
  • Software Developer
  • Application Architect
  • Enterprise Architect
  • DevOps Engineer

However, as in most professional disciplines, there is no common definition of what these jobs actually do. So although in common usage, referring to a job title in isolation can be confusing and does not help organisations recruit, develop, deploy, manage and retain their valuable talent. 

Using SFIA to improve Big Data/Data Science job descriptions.

SFIA provides a common language of skills and skill levels. It is very flexible and this enables organisations to design their own team structures, roles and job titles.

They can then select the appropriate configuration of SFIA skills and competency levels to match. 

  • The SFIA skills for roles responsible for big data and data science should be selected based on an analysis of the role's accountabilities and responsibilities. 
  • To provide the necessary focus, aim for no more than 6 to 8 SFIA skills per role (less if possible) 
  • The required skills can be selected from the full range of skills in SFIA 
  • The skill levels chosen should be based on the responsibility levels of the role and aligned to SFIA's generic attributes for levels of responsibility

Focussing on job responsibilities, and the SFIA-defined professional skills and responsibility levels provide a much clearer definition of the requirements of the job. This, in turn, supports people management related activities such as recruitment, skills assessment, professional development, and performance management.

You should capture Knowledge requirements for jobs separately

Depending on the nature of the job this could include ...

  • Knowledge of relevant data management, machine learning, data visualistion applications, languages, environments, systems software, packages, platforms
  • Broad knowledge of a range of  software development and delivery lifecycles
  • Knowledge of different vendors’ products, pros & cons of industry data management tools 
  • Knowledge of information security and privacy
  • Agile product development techniques 
  • Application of devops methods and tools to managing machine learning models
  • Business domain an industry knowledge

Standard or model skills profiles for Big data/Data science roles

The SFIA Foundation is looking to develop and publish a set of standard skills profiles for some of the common industry roles. This is work in progress and if you would like to know more and/or contribute please contact the .