Table of contents
- Who is a data analytics engineer?
- Role of a data analytics engineer
- Key skill requirements to succeed in 2022
- Salary range
- Conclusion/Key takeaways
A pipeline of technical staffing roles has been established to handle the collection and processing of large amounts of data.
If you will, a data engineer is down in the engine room. He integrates many sources of information and manages the processes that make and maintain the data available for business analysis.
The data analyst is at the top of the pyramid, who provides the data from mostly pre-formed models to nontechnical business users so that they may execute their tasks.
The data analytics engineer is on the middle-deck, between these two. This is a specialist who understands both data engineering technology and business data analysis requirements, and thus can create the analytical models that upper-deck data analysts and business end users require to perform their roles.
A data analytics engineer is a person who blends the abilities of a data analyst and a software engineer to source and manipulate data for quick analysis. Because of their technical ability and business acumen, they have become extremely valuable as members of the data team. This article discusses the roles of the analytics engineer and their remuneration prospects.
Who is a data analytics engineer?
The analytics engineer is a member of a data team that is responsible for efficient, integrated data models and solutions. They create useful, well-tested and documented dataset representations and tools that the rest of the company can use to answer their questions.
They move and transform data from the source so that it may be easily analyzed, visualized, and worked upon by the data analyst or business user. Not only that, but they have the technical expertise to apply software engineering best practices such as Version Control and CI/CD, but also must communicate effectively with stakeholders on the use of these tools.
Data analytics engineers combine business strategy and technical data expertise to translate complex data into visual representations known as data models. They collaborate with data analysts and data engineers to provide simple visual representations of data patterns and convey their meaning to coworkers, stakeholders, and end-users.
The shift to cloud data warehouses, the proliferation of self-service business intelligence (BI) tools, and the introduction of data ingestion tools have contributed to significant shifts in data tooling.
Data now falls in the warehouse before it has been transformed as a result of the shift to an extract, load, and transform (ELT) process. This opens the door to skilled technical analysts who are both familiar with the business and possess the technical knowledge required to model the raw data into neat, well-defined datasets.
Analytics engineers handle the data themselves, as well as organizing and sorting data. It is their job to ensure that data is absorbed, transformed, scheduled, and ready to be used for analytics by all who may need it. Many analytics engineers are the orchestrators of the modern data stack, and they choose and apply tools for ETL/ELT.
A data analytics engineer's role
The analytics engineer is responsible for constructing and managing a data warehouse to store data. They also select the best methods to store data from many sources in this warehouse. Then they model the data to be used by analysts and schedule tests to simplify these models.
Engineers are responsible for ingesting data into the warehouse and ensuring that databases remain current. They are the first to be notified of any issue in the pipeline, so they can correct it.
This is the process of constructing visual representations of data and relating connections between different information locations and systems. Analytics engineers are charged with modeling raw data into datasets that enable analytics across the company. These datasets act as a central source of truth, making it simpler for business analysts and other stakeholders to view and understand data in a database.
The engineer manages data pipelines and workflows to move data from one point to another and coordinates the combining, verifying, and storage of that data for analysis.
Whether by recommending strategies for better SQL, reorganizing a dataset to include a new dimension or training them on software engineering best practices, dataops (a methodology that includes data engineering, data analytics, and devops). Some best practices that can be improved include version control, data unit testing, and continuous integration and continuous delivery (CI/CD).
As a member of a team, they collaborate with colleagues to analyze business requirements, define feasible analytics outcomes, and construct data models.
A data analytic engineer may be required to perform some or all of the following tasks depending on the organization and role specifications:
- Collaborate with product, engineering, data science, strategy and client teams to understand client needs and deliver actionable solutions.
- Transform raw data into actionable analytical information and business logic.
- Interface directly with other engagement teams to present analyses to answer their key business questions.
- Combine data mastery with industry expertise to scope and implement projects using relevant datasets.
- Seek areas for functional improvement and take initiative to appropriate them.
- Provide advanced analysis, insights and data-driven recommendations to internal teams and other concerned stakeholders.
The analytics engineer gathers information, models data, maintains data documentation, collaborates with data team members, and communicates results to interested parties. Therefore, the Analytics Engineer combines business acumen with technical knowledge.
In 2022, there are two key skills prerequisites for you to succeed.
Almost every business or employer seeks out an analytics engineer because to a certain set of abilities, but there are also a few general abilities and competencies that are critical for every analytics engineer. These skills are discussed later.
Analytic engineers use SQL to modify data models. It is one of the most essential skills that you must master to become an analytics engineer, since the majority of your time spent designing data models is spent writing queries.
In the language it is used, SQL is closely related to Dbt, and therefore knowledge of the former is required for the latter. Dbt is the industry's leading data transformation tool, which is why it is most likely that the majority of analytics engineers use it to write their data models.
Analytics engineers must understand advanced programming languages such as R and Python to perform various data orchestration tasks. Python is used by many data pipeline programs, and being proficient in it is extremely useful for developing your own pipeline.
An analytics engineer must be familiar with the most popular data stack tools: ingestion, transformation, storage, and deployment tools: if not extensive knowledge, then at least basic concepts behind each of them. Learning one tool in each component of the stack may facilitate an inference of the others.
An engineer must have expertise with data pipelines. Some examples include data warehouses like Snowflake, Amazon Redshift, and Google BigQuery, as well as business intelligence tools like Tableau, Looker, and others.
Analytics engineers must maintain ongoing communication with relevant individuals when data quality is harmed or a pipeline is damaged, and to understand what the business requires. If this isn't done, costly information and opportunities will be lost.
To succeed, an analytics engineer must possess a strong combination of technical skill and stakeholder management abilities.
The salary range varies widely.
Analytics engineers in all industries and environments now have excellent prospects with competitive pay scales. According to Glassdoor, the average base salary is $91,188 and $111,038 per year in the United States.
The analytics engineer is charged with modeling data to provide neat and accurate datasets so that different users within and outside the organization may understand and utilize them. The job involves gathering, transforming, testing, and documenting data. It requires essential skills in communication, software engineering, and programming.
The role of the analytics engineer is relatively new to the data analytics industry, but it is rapidly gaining traction and recognition as more and more people realize its value.