Here are the main jobs in the world of data classified according to three families: data management, data analysis and finally, data governance. Some professions have existed almost since the beginning of computing, while others are much more recent.
Data management roles
Data management includes all the activities that enable the concrete use of data.
Data architects are technical experts in charge of designing the software and technical architecture of the information systems that support the company’s data.
They have a great knowledge of the different data storage and processing technologies available on the market. They know how to choose and assemble them in order to design a software architecture, or even an enterprise architecture, capable of meeting present and future challenges in terms of data management.
For a few years now, and especially since the advent of Big Data, the job has become more complex…
- Mastery of the main principles of software, technical or enterprise architecture
- Mastery of data storage and processing technologies
- Team spirit
- Computer science, mathematics…
Data engineers are software developers specialized in data. They design and implement the collection, databases, storage and processing that provide us with data that can be used by the company’s various applications, data scientists, data analysts, etc.
Data engineers appeared at the beginning of the 2010’s with big data, and are particularly skilled in big data techniques that allow them to manage huge volumes, velocities and varieties of data.
Data engineers are generally required to follow the architectures defined by the data architects within the company. They work closely with the data scientists and data analysts for whom the prepared data is primarily intended.
- Mastery of big data processing technologies
- Mastery of big data programming languages, such as Python, Scala or Java
- Rigor and tenacity
- Team spirit and organization
- Computer engineering, big data or statistics
- Computer design and development engineer, developer
Data custodians administer and monitor one or more data sources. They must ensure maintenance, accessibility, quality and safety. They are therefore in charge of the technical part of the project, as close as possible to the databases.
The role of data custodian is often the technical counterpart to that of data steward.
- Mastery of database languages
- Good IT culture
- Knowledge of data security and quality
- Computer Science
- Database administrator
Data analysis professions
Data analysis is downstream of data collection and preparation to extract value from the data. Historically, there have been three generations of analysis:
|Descriptive analysis||What’s going on?||business intelligence|
|Predictive analysis||What will happen?||big data, data analytics|
|Prescriptive analysis||Why is this happening and how can we change the future?||big data, artificial intelligence, data science|
Data analysts mine raw data to extract information of value to the business. They will reveal indicators, statistics, trends… in order to produce crucial analyses and interpretations for decision support. They often present the results of their analyses in the form of data visualization.
Since the emergence of big data and artificial intelligence, the business requires increasing technical expertise.
- Mastery of data analysis technologies
- Strong statistical skills
- Strong analytical skills
- Mastery of reporting and visualization tools
- Spirit of analysis, spirit of synthesis
- Intellectual curiosity
- Statistics, econometrics, big data
Business Intelligence Manager
Business intelligence managers are responsible for all business intelligence techniques, including multidimensional analysis and visual reporting. Less technical than data analysts, with whom they sometimes work, their role is above all to produce descriptive analyses for decision-makers.
- Relational skills
- Written and oral communication
- Business Management
- Business Analyst
Data scientists are experts in data analysis who have the technical skills to solve complex problems. This profession has developed particularly with big data and artificial intelligence.
They generally have the skills of data analysts and can also develop predictive models using advanced statistical techniques such asmachine learning.
They are the architects of predictive and prescriptive analysis.
- Mastery of data science tools and programming in Python
- Understanding of business issues and use cases
- Strong mathematical and statistical skills
- Creativity and intellectual curiosity
- Analytical skills
- PhD or master in mathematics, statistics
- School of Statistics or Computer Engineering
- Machine learning engineer: specialist in machine learning
- Deep learning engineer: specialist in artificial neural networks
Data miners look for new data inside or outside the company to bring new opportunities. This is commonly referred to as data mining. They master the techniques of data collection, segmentation and modeling. Their intellectual curiosity is an essential quality.
- Mastery of data mining and discovery tools
- Knowledge of the repositories – paying or open data – of the business domain
- Knowledge of modeling and data analysis techniques
- Analytical and observational skills
- Business, statistics, business intelligence, econometrics
Data governance roles
Recent advances in data management and analytics have created a need for specialized data governance, which is leading to new roles.
Chief Data Officer
This is the highest ranking data job in an organization. Their mission is to create a framework for all managers to get value from the data. Generally speaking, they oversee all data-related activities from a strategic and organizational point of view.
- Good understanding of the company’s business and its data assets
- Excellent data literacy
- Good knowledge of the functionalities of analysis and data management tools
- Leadership: inspiring, diplomatic and charismatic
- Strong managerial skills: listening, teaching and communication
- Business, commerce, management, marketing, statistics
- Additional training in data
- Chief Information Officer: more business and less technical
- Head of Data Governance: focuses on data governance and reports to the Chief Data Officer.
- Chief Digital Officer: more digital and more focused on applications; Chief Data Officers tend to replace them because of the data centric approach.
Data Protection Officer
Data protection officers are responsible for the regulatory compliance of data. They must ensure that the company’s data complies with transparency, privacy and security laws.
Since 2018, European companies and administrations that process personal data on a large scale are required to appoint a data protection officer to enforce citizens’ rights, under the GDPR.
- Legal expertise of the data
- Knowledge of the basics of computer science and data
- Independence: the regulation prevails in all circumstances…
- Data protection, computer security
The title of data owner is more of a data governance hat than a full-time role.
Data owners are the business managers who make decisions and approve data in their domain: finance, sales, marketing, human resources, supply chain management… They align strategic objectives and data. They are the ones who express the need for new data or improvements to existing data.
- In-depth understanding of their field
- Data Literacy
- Collaborative spirit, especially with data owners from neighboring domains
- Management of other data governance hats and roles
Like data owner, the title of data steward is more of a data governance hat than a full-time role.
Data stewards are responsible for the day-to-day management of one or more data sources, their documentation in the data catalog and their quality: they are the data stewards.
In addition, they generally work as product managers, product owners or business analysts in connection with one or more applications. Some have business skills; others are more technical. Ideally, a data source is managed by business and technical data stewards.
- Understanding the business
- Data Literacy
- Data modeling
- Master data management
- Data quality management
- Collaborative spirit with other data governance actors