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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 staffing roles has been developed to manage the collection and processing of that data.

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

On the top deck is the data analyst, who serves the data from largely pre-formed models to nontechnical business users so they can perform their work.

Mid-deck, between these two, is the data analytics engineer. This is a specialist who understands both data engineering technology and the data analysis needs of a business, and thus can build the analytical models that the upper-deck data analysts and business end users need to fulfill their roles. 

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Therefore, a data analytics engineer is a person who combines the skills of the data analyst and software engineer to source and transform data for easy analysis. Because of their technical dexterity and business acumen, they have become quite valuable as members of the data team. This article details the duties and requisite skills of the analytics engineer, as well as the remuneration prospects of the role.

Who is a data analytics engineer?

The analytics engineer is a member of a data team who is responsible for efficient, integrated data models and products. They build 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 can be easily analyzed, visualized and worked upon by the data analyst or business user. Not only that, but they have the technical skills to apply software engineering best practices such as Version Control and CI/CD, but also need to communicate effectively with stakeholders about the use of these tools.

The datasets created by a data analytics engineer allow end-users to comprehend and examine the information within the data. An analytics engineer combines business strategy and technical data knowledge to translate complex information and illustrate them clearly as visual representations known as data models. They collaborate with data analysts and data engineers to provide simple visual representations of data patterns and communicate their meaning to coworkers, stakeholders and end-users.

The transition to cloud data warehouses, evolution of self-service business intelligence (BI) tools and 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 drops in the warehouse before it has been transformed. This creates an opportunity for skilled technical analysts who are both  well-versed with the business and the technical skills required to model the raw data into neat, well-defined datasets. This requires the skills of both a software engineer and a data analyst, which the analytics engineer possesses.

Analytics engineers handle the data itself, as well as managing and sorting data. It is their job to make sure data is ingested, transformed, scheduled and ready to be used for analytics by all who may require it. Many analytics engineers are the orchestrators of the modern data stack, and they decide on and apply tools for 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 ingest data from different sources into this warehouse. Then they model the data to be used by analysts and schedule tests to simplify these models. The basic duties of the analytics engineer include:

1. Data warehouse management

Engineers are responsible for ingesting data into the warehouse and making sure that datasets are maintained. They are the first to be notified of any issue 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 charged with modeling raw data into datasets that enable analytics across the company. These datasets 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 orchestration

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

4. Setting best practices

They enable other team members like data analysts and data scientists to be more effective. Whether by sharing tips for writing better SQL, reworking a dataset to contain a new metric or dimension, or training them on how to apply best practices for software engineering. This approach is called dataops (a methodology that integrates data engineering, data analytics and devops). A few best practices that can be optimized include version control, data unit testing as well as continuous integration and continuous delivery (CI/CD).

5. Inter-collaboration

As a member of a team, they collaborate with team members to collect business requirements, define successful analytics outcomes and design data models.

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

  • 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 collects information, designs data models, writes code, maintains data documentation, collaborates with data team members and communicates results to concerned stakeholders. Therefore, the Analytics Engineer blends 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 out for a specific set of skills that they require in an analytics engineer, but some general skills and competencies are vital for every analytics engineer. These skills are discussed subsequently.

SQL and DBT Prowess

Analytic engineers typically use SQL to write transformations within data models. SQL is one of the most important skills that you need to master to become an analytics engineer, since the major portion of the analytics engineer’s duties is creating logic for data transformations, writing queries and building data models. 

SQL is closely related to Dbt in the language it utilizes, 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 most likely that the majority of analytics engineers use this to write their data models.

Programming languages

Knowledge of advanced languages like R and Python is crucial for analytics engineers to handle various data orchestration tasks. Many data pipeline tools utilize 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 conversant with the most popular tools in a modern data stack. This means possessing experience with ingestion, transformation, warehousing and deployment tools: if not comprehensive knowledge of them, then at least the basic concepts behind each of them. Learning one tool in each part of the stack may facilitate inferential understanding of the others.

Data engineering and BI tools knowledge

An engineer needs to have experience with tools for building data pipelines. Some of these tools include data warehouses like 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 because it is their responsibility to ensure that everyone is updated on the status 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 collaborate with business teams and data analysts to understand what the business needs. If this isn’t done, erroneous assumptions can be made on defective data, and valuable ideas and opportunities will go unnoticed. It is imperative for an analytics engineer to develop and sustain multi-functional interactions with various teams across the business.

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

Salary range

Analytics engineers in all industries and environments now have great prospects with good remuneration scales. According to Glassdoor, the average base salary is $91,188 and $111,038 in total annually in the U.S.

Conclusion/Key takeaways

The analytics engineer is tasked with modeling data to provide neat and accurate datasets so that different users within and outside the company can understand and utilize them. The role involves gathering, transforming, testing and documenting data. It requires key skills in terms of communication, software engineering and programming.

The role of the analytics engineer is fairly new to the data analytics niche, but it is fast gaining traction and recognition as more and more people realize its worth.

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