Technology Data Analytics Engineer: Defining the Role and Skill Requirements

Data Analytics Engineer: Defining the Role and Skill Requirements


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As large amounts of data, from both external and internal data sources, have become central to running an organization, a pipeline of technical staff functions has been developed to manage the collection and processing of that data.

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

On the top deck is the data analyst, who serves data from mostly pre-built models to non-technical business users so they can do their job.

Middle deck, in between these two, is the data analytics engineer. This is a specialist who understands both data engineering technology and a company’s data analytics needs, thus building the analytical models that the data analysts and business end users on the upper deck need to fulfill their roles.


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Therefore, a data analytics engineer is one who combines the skills of the data analyst and software engineer to source and transform data for easy analysis. Because of their technical agility and business acumen, they have become quite valuable as members of the data team. This article describes the analytics engineer’s duties and required skills, as well as the role’s reward 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 products. They build useful, well-tested, and documented representations of data sets and tools that the rest of the business can use to answer their questions.

They move and transform data from the source so that it can be easily analysed, visualized and edited by the data analyst or business user. Not only that, but they also have the technical skills to apply software engineering best practices such as version control and CI/CDbut also communicate effectively with stakeholders about the use of these tools.

The data sets created by a data analytics engineer enable end users to understand and explore the information contained in the data. An analytical engineer combines business strategy and technical data knowledge to translate and clearly illustrate complex information as visual representations known as data models. They work with data analysts and data engineers to provide simple visual representations of data patterns and communicate their meaning to colleagues, stakeholders and end users.

The transition to cloud data warehouses, the evolution of self-service business intelligence (BI) tools, and the introduction of data ingestion tools have contributed to significant shifts in data tooling. Roles and responsibilities within traditional data teams are changing.

With the shift to an extract, load, transform (ELT) procedure, data now falls into the warehouse before being transformed. This creates an opportunity for skilled technical analysts who are both well-versed in the business and have the technical skills needed to model the raw data into neat, well-defined data sets. This requires the skills of both a software engineer and a data analyst, which the analytics engineer possesses.

Analytics engineers process the data themselves, as well as manage and sort data. Their job is to ensure that data is ingested, transformed, planned, and ready to be used for analysis by anyone who needs it. Many analytics engineers are the directors of the modern data stack, deciding and applying tools to: ETL/ELT.

Role of a data analytics engineer

The analytics engineer is responsible for implementing and managing a data warehouse to ingest data. They also decide on the best tools to incorporate data from various sources into this warehouse. They then model the data to be used by analysts and plan tests to simplify these models. The basic tasks of the analytics engineer include:

1. Data warehouse management

Engineers are responsible for recording data in the warehouse and ensuring data sets are maintained. They are the first to be notified of a problem in the pipeline so they can fix it.

2. Data Modeling

This is the process of building visual representations of data and relating connections between different information locations and systems. Analytics engineers are tasked with modeling raw data into datasets that enable analytics across the business. These data sets act as a central source of truth, making it easier for business analysts and other stakeholders to view and understand data in a database.

3. Data Format

The engineer creates data pipelines and workflows to move data from one point to another, and coordinates combining, verifying, and storing that data for analysis. The engineer understands everything about data orchestration and automation.

4. Set up best practices

They enable other team members such as data analysts and data scientists to be more effective. Whether it’s sharing tips to write better SQL, reworking a dataset to include a new metric or dimension, or training them in applying software engineering best practices. This approach is called dataops (a methodology that integrates data engineering, data analytics and devops). Some best practices that can be optimized include version control, data unit testing, and continuous integration and continuous delivery (CI/CD).

5. Inter-cooperation

As part of a team, they work with team members to gather business requirements, define successful analysis results, and design data models.

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

  • Collaborate with product, engineering, data science, strategy, and customer teams to understand customer needs and deliver actionable solutions.
  • Transform raw data into actionable analytical information and business logic.
  • Connect directly with other engagement teams to present analytics to answer their key business questions.
  • Combine data mastery with industry expertise to scope and implement projects using relevant data sets.
  • Look for areas for functional improvement and take the initiative to appropriate them.
  • Provide advanced analytics, insights, and data-driven recommendations to internal teams and other involved stakeholders.

The analytics engineer collects information, designs data models, writes code, maintains data documentation, collaborates with data team members and communicates results to involved stakeholders. That is why the Analytics Engineer combines business acumen with technical expertise and alternates between business strategy and data development.

Key Skill Requirements to Succeed in 2022

Every company or employer looks for a specific set of skills they need in an analytics engineer, but some common skills and competencies are vital for any analytics engineer. These skills are then discussed.

SQL and DBT proficiency

Analytical engineers typically use SQL to write transformations within data models. SQL is one of the most important skills to master to become an analytics engineer, as the main part of the analytics engineer’s duties is to create logic for data transformations, write queries, and build data models.

SQL is closely related to Dbt in the language it uses, so knowledge of the former is required for the latter. Dbt is the leading data transformation tool in the industry, which is why it is highly likely that the majority of analytics engineers use it to write their data models.

programming languages

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

Modern data stack tools

An analytics engineer needs to be familiar with the most popular tools in a modern data stack. This means having experience with recording, transformation, warehousing and implementation tools: if not extensive knowledge of them, then at least the basic concepts behind each of them. Learning one tool in each part of the stack can facilitate inferential understanding of the others.

Knowledge of data engineering and BI tools

An engineer should have experience with 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 essential for analytics engineers, as it is their responsibility to ensure that everyone is aware of the state of data. They need to communicate with relevant individuals when data quality is compromised or when a pipeline is damaged to understand what the business needs. They also need to work with business teams and data analysts to understand what the business needs. Failure to do so can lead to wrong assumptions being made on flawed data, and valuable ideas and opportunities go undetected. It is imperative for an analytics engineer to develop and maintain cross-functional interactions with different teams across the company.

In short, an analytics engineer must have a robust combination of technical agility and stakeholder management skills to succeed.

Salary scale

Analytics engineers in all industries and environments now have great prospects with good pay scales. According to Glass doorthe average base salary is $91,188 and $111,038 total annually in the US

Conclusion/Key takeaways

The analytical engineer is tasked with modeling data to deliver neat and accurate data sets so that different users inside and outside the company can understand and use them. The role includes collecting, transforming, testing and documenting data. It requires important communication, software engineering and programming skills.

The role of the analytics engineer is fairly new to the data analytics niche, but is rapidly gaining traction and recognition as more and more people see its value.

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Shreya Christina
Shreya has been with for 3 years, writing copy for client websites, blog posts, EDMs and other mediums to engage readers and encourage action. By collaborating with clients, our SEO manager and the wider team, Shreya seeks to understand an audience before creating memorable, persuasive copy.


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