Data Analytics Engineer: Defining Roles and Skill Requirements

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As large amounts of data, from both external and internal data sources, became central to running an organization, a system of technical staff roles was developed to manage the work. collect and process that data.

In the engine room, if you will, be a data engineer who integrates multiple data sources and manages operations that make and keep data available for business analysis.

At the top tier is a data analyst who serves up data from mostly pre-made models to non-technical business users so they can do their jobs.

Mid-deck, between these two, is the data analysis engineer. This is a professional who understands both data engineering technology and a business’s data analytics needs, so can build analytical models that upper-tier data analysts and end-users alike. business needs to fulfill their role.


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Thus, a data analyst is someone who combines the skills of a data analyst and a software engineer to source and transform data for easy analysis. Thanks to their technical ingenuity and business acumen, they have become quite valuable as members of the data team. This article details the required duties and skills of an analytical engineer, as well as the remuneration outlook for the role.

Who is a data analysis engineer?

Analytical engineers are members of the data team responsible for efficient, integrated data models and products. They build useful, well-tested, and documented data set 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 data analysts or business users can easily analyze, visualize, and work with. Not only that, they also have the technical skills to apply software engineering best practices like Version Control and CI / CDbut also need to effectively communicate with stakeholders about the use of these tools.

Data sets created by a data analysis engineer allow the end user to understand and examine the information in the data. An analytical engineer combines business strategy and technical data knowledge to translate complex information and illustrate them clearly in the form of a visual representation known as a data model. They collaborate with data analysts and data engineers to provide simple visual descriptions of data patterns and communicate their meaning to colleagues, stakeholders, and end users.

The shift to cloud data warehouses, the growth of self-service business intelligence (BI) tools, and the introduction of data entry tools have all contributed to significant changes in data tools. Whether. Roles and responsibilities in the traditional data team are changing.

With the move to an extract, load, transform (ELT) procedure, data will now be reduced in storage before it is converted. This provides the opportunity for skilled technical analysts, who have both business expertise and the technical skills needed to model raw data into neat, well-defined data sets. clearly defined. This requires the skills of both the software engineer and the data analyst that the analytics engineer possesses.

Analytical engineers process the data themselves, as well as manage and organize it. Their job is to ensure that data is entered, transformed, scheduled, and ready to use for analysis by everyone who may require it. Many analytical engineers are modern data stack coordinators and they decide and apply tools to ETL / ELT.

The role of a data analysis engineer

Analytical engineers are responsible for implementing and managing data warehouses for data entry. They also decide on the best tools to import data from different sources into this repository. They then model the data that will be used by analysts and schedule tests to simplify these models. The basic duties of an analytical engineer include:

1. Data warehouse management

Engineers are responsible for entering data into the warehouse and ensuring that data sets are maintained. They are the first to be notified of any problems in the pipeline, so they can fix it.

2. Data Modeling

It is the process of building visual representations of data and related connections between different locations and information systems. Analytical engineers are responsible for modeling raw data into datasets that enable company-wide analysis. These datasets act as a central source of truth, making it easier for business analysts and other stakeholders to view and understand the data in the database.

3. Data coordination

Engineers create data pipelines and workflows to move data from one point to another, and coordinate combining, verifying, and storing that data for analysis. Engineers understand everything about data orchestration and automation.

4. Set best practices

They allow other team members such as data analysts and data scientists to be more productive. Whether by sharing tips for better writing SQL, rework the dataset to accommodate new metrics or dimensions, or train them on how to apply software engineering best practices. This approach is called dataops (an approach that integrates data engineering, data analytics, and devops). Some of the best practices that can be optimized include version control, data unit testing, and continuous integration and continuous delivery (CI/CD).

5. Coordination between the

As team members, they collaborate with team members to gather business requirements, determine successful analysis results, and design data models.

Depending on the company and role specifications, a data analytics engineer may be required to do some or all of the following:

  • Collaborate with product, engineering, data science, strategy, and customer teams to understand customer needs and deliver useful solutions.
  • Transform raw data into actionable analytics and business logic.
  • Communicate directly with other participating groups to present analytics to answer their key business questions.
  • Combine data proficiency with industry expertise to scale and execute projects using relevant datasets.
  • Look for areas for functional improvement and proactively correct them.
  • Deliver advanced analytics, insights, and data-driven recommendations to internal teams and other stakeholders.

Analytical engineers collect information, design data models, write code, maintain data documentation, collaborate with data team members, and communicate results to stakeholders. As a result, Analytical Engineers combine business acumen with technical expertise and alternate between business strategy and data development.

Key skills required for success in 2022

Every company or employer is looking for a specific set of skills that they require in an analytical engineer, but some general skills and competencies are vital to every analytical engineer. These skills will be discussed later.

The Power of SQL and DBT

Analytical engineers often use SQL to write transformations in data models. SQL is one of the most important skills you need to master to become an analytical engineer, as the main part of an analytical engineer’s job is to create logic for data transformations, write queries, and build build data models.

SQL is closely related to Dbt in the language it uses, so knowledge of the former is necessary for the latter. In debt is the industry leading data transformation tool which is why most likely the majority of analytical engineers use it to write their data models.

Programming language

Knowledge of advanced languages ​​such as R and Python is crucial for analytical engineers to handle various data orchestration tasks. Many data pipeline tools use Python, and knowing how to code in it will be extremely helpful for writing your own pipeline as an engineer.

Modern data stack tools

An analytical engineer needs to be fluent in the most common tools in a modern data stack. This means having experience with import, conversion, warehousing, and deployment tools: if not comprehensive knowledge of them, then at least the basic concepts behind each. that tool. Learning one tool in each section of the stack can facilitate an exponential understanding of others.

Knowledge of data engineering and BI . tools

An engineer needs experience in data pipeline building tools. Some of these tools include data warehouses such as Snowflake, Amazon Redshift, and Google BigQuery; ETL tools like AWS Glue, Talend, or others – as well as business intelligence tools like Tableau, Looker, etc.

Communication and interpersonal skills

Communication is key for analytics engineers as they are responsible for ensuring that everyone is up to date with the state of the data. They need to communicate with the individuals involved when data quality is compromised or when the pipeline is damaged, to understand what the business needs. They also need to collaborate with business teams and data analysts to understand what the business needs. If this is not done, false assumptions can be made on faulty data and valuable ideas and opportunities go unnoticed. Analytical engineers are required to develop and maintain cross-functional interactions with different teams in the enterprise.

In short, an analytical engineer must have a strong combination of technical skills and stakeholder management skills to be successful.


Analytical engineers in all industries and environments now have great prospects with good salaries and bonuses. Based on Glass doormedian base salary is $91,188 and totals $111,038 annually in the US

Conclusion / Key Lessons

Analytical engineers are tasked with modeling data to provide neat and accurate data sets so that different users inside and outside the company can understand and use them. The role involves collecting, transforming, examining, and recording data. It requires key skills in communication, software engineering and programming.

The role of an analytics engineer is relatively new to the field of data analytics, but it is rapidly gaining traction and recognition as more and more people realize its value.

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