Analytics engineer - Salaries, roles, skills, prospects

The analytics engineer is one of the new data professions. Appearing long after the triptych data analyst - data engineer - data scientist; it is in 2018 that it emerges and today more and more offers of Analytics engineer bloom in companies. This profession, sometimes mistakenly spelled "Analytic Engineer", has become an essential pillar in business, offering a decision-making approach. The median gross annual salary for an Analytics Engineer ranges from €44,000 for beginners to €60,000 for experienced profiles (2 to 5 years' experience). This profession straddles the border between Data Analyst and Data Engineer positions.

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The missions of the Analytics Engineer

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Unlike the Data Analyst, who spends more time on data analysis, the Analytics Engineer concentrates his efforts on modeling data to make it more accessible to end-users. Thanks to this method, end-users are able to answer the majority of their data-related questions themselves. An experienced Analytics Engineer will therefore be called upon to transform, test, deploy and document the data for which he or she is responsible.

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Skills required to become an Analytics Engineer

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In order to master the subtleties of the business, a few key skills must be acquired:

● Mastery of various programming languages, in particular SQL, Python

● A key tool for Analytics Engineers is DBT (Data Build Tool), which makes the mention of "dbt Analytics Engineer" relevant. This tool helps to transform and organize data efficiently and has become a sought-after skill in job descriptions for this profession.

● Good communication skills to ensure effective collaboration with both technical and business teams

● Familiarity with modern data tools, including cloud, ETL, version management, etc.

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How much does an Analytics Engineer earn?

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A key question for aspiring and current Analytics Engineering professionals is salary. The salary of an Analytics Engineer is influenced by several factors, including supply and demand in the sector, geographical location, and the professional's level of experience.

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Factors influencing salary

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● Supply and Demand: As in any profession, salary is partly determined by the law of the market. High demand for Analytics Engineering skills can lead to higher salaries.

● Geographical location: Location has a significant impact. For example, salaries in major metropolises or regions with a developed technology sector are generally higher.

● Level of experience: Accumulated experience in the trade also plays a crucial role. Advanced skills, certifications and practical experience can significantly increase earning potential.

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Salary scale in Paris

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● Beginners : For an Analytics Engineer at the start of their career, salaries can range from €38,000* to €50,000*.

● Confirmed (with 2 to 5 years' experience): can expect a salary of between €50,000* and €60,000*.

● Experts: With experience and the development of specialized skills, an Analytics Engineering expert can expect a salary of up to €85,000*. This higher level is often reserved for professionals with several years' experience, demonstrating advanced mastery of tools and methodologies, as well as an ability to manage complex projects and deliver innovative solutions.*(gross salary per annum)

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Salary prospects

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As the field of data science continues to evolve and the importance of data analysis in business decisions increases, the demand for qualified Analytics Engineers is set to remain strong.

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How can I become an Analytics Engineer?

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There are several options for starting out on the path to becoming an Analytics Engineer. There is no single path, and you can opt for masters degrees in computer engineering or statistics applied to data. In addition to university courses, some business and engineering schools offer suitable courses. If enrolling in a school puts you off, you should know that there are also intensive courses and bootcamps (Databird, Datascientest...). Finally, you can enrich your training with various programming certifications on platforms such as Coursera, edX or Udacit. In any case, before choosing a program, make sure that it offers high-quality, relevant teaching and the (often overlooked) possibility of gaining practical experience through internships, projects or even work-study programs.

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Differences between Analytics Engineer and Data Engineer

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The Data Engineer focuses on the design and management of data infrastructures, processing raw data to make it usable. Their main objective is to ensure data availability and readiness. The analytics engineer, on the other hand, transforms the data to make it comprehensible to everyone. They create interactive dashboards and visual reports, making data easier to understand for everyone. Data Engineers prepare the ground for analysis, while Analytics Engineers focus on creating analytical tools to facilitate decision-making. Although distinct, these two roles form a complementary team, guaranteeing an efficient and exploitable flow of data.

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Case study: Brandon Thompson, an inspiring Analytics Engineer

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Currently working at dbt, Brandon Thompson is a perfect example of the role of Analytics Engineer. Having actively participated in the development of this profession within DBT, he now puts his expertise at the service of the data community as an evangelist and advocate of the discipline.

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To sum up:

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📊 Analytics Engineer

● New position in the data world for 5 years now.

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🚀 Role and importance

● Accelerate the use of data in companies.

● Transforms and provides end-users with their own data.

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🔗 Positioning

● Between Data Engineer and Data Analyst.

● Tasks: transform, test, document, deploy Data Pipelines.

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💻 Technical skills

● Mastery of SQL, Python, and tools such as Snowflake, Git.

● Uses Modern data stack practices (e.g. DBT, Fivetran, Airbyte, Looker).

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👥 Interaction with other roles

● Collaborates with Data Engineers and Data Analysts to optimize the use of data.

● Helps to understand and add value to data on the business side.

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📈 Future prospects

● Position promoted by DBT Lab, creators of DBT.

● Will become important for reducing the "time to market" of data.

● Promotes data culture in companies.

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Conclusion

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The Analytics Engineer is a crucial link in the world of data, offering a unique specialization and skills that are increasingly in demand. With an attractive salary and a clear distinction from the Data Engineer role, this profession is booming, both in terms of market demand and strategic importance for companies.

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