What Is an Analytics Engineer?

Analytics engineers make sure that companies can understand their data and use it to solve problems, answer questions, or make decisions. According to the World Economic Forum's The Future of Jobs Report 2023, one of the key factors influencing job growth around the world is the rising importance of big data analytics [1].

During the last decade, a number of new jobs involving data have appeared around the world, including analytics engineer. Read on to explore analytics engineering careers, what kind of salary you might make, and how to get started in this interesting field.

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What is an analytics engineer?

An analytics engineer manipulates raw data to make it more available, organised, and easier to analyse. Working with other data-related professionals like data analysts, data engineers, and data scientists, analytics engineers help companies make better decisions, lower costs, improve productivity, and increase revenue.

Data engineer vs analytics engineer

An analytics engineer acts as a link between data engineers and data analysts. For comparison, data engineers work on designing the platforms and processes for analytics engineers to use. Analytics engineers then use the platforms and processes to clean, organise, and maintain data, and data analysts examine and interpret the data.

To prepare data for data analysts and other key figures in a company, an analytics engineer engages in data testing, data transformation, deployment of data models, and data documentation.

What does an analytics engineer do?

To better understand the role of an analytics engineer, it helps to know what an employer might expect in terms of tasks and responsibilities. Daily duties might include:

• Collaborating with data scientists, data engineers, and data analysts

• Deploying logical and physical data models

• Using business intelligence (BI), data science, and visualisation tools to build data products

• Building automated and scalable data pipelines

• Providing insight and recommendations to the data team and company stakeholders

• Working independently across various projects and as part of a Scrum team

• Using software development best practices like code review, continuous integration and continuous delivery (CI/CD), release management, and version control

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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(78,119 ratings)

698,207 already enrolled

Beginner level

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Average time: 4 month(s)

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Skills you'll build:

Generative AI, Data Science, Model Selection, Data Analysis, Python Programming, Data Visualization, Predictive Modelling, Numpy, Pandas, Dashboards and Charts, dash, Matplotlib, Cloud Databases, Relational Database Management System (RDBMS), SQL, Jupyter notebooks, Machine Learning, Clustering, regression, classification, SciPy and scikit-learn, CRISP-DM, Methodology, Data Mining, Github, Jupyter Notebook, K-Means Clustering, Data Science Methodology, Rstudio, Big Data, Deep Learning, Quering Databases, Data Generation, Career Development, Interviewing Skills, Job Preparation, Resume Building

Analytics engineer salary and job outlook

According to Glassdoor, the average annual base salary for an analytics engineer totals ₹10,69,374, with a salary range of ₹7L to ₹16L based on experience and other factors [2]. If an employer provides profit sharing or bonuses, you may have an opportunity to make a higher annual salary as well.

The global advanced analytics market should grow at a CAGR of more than 21 per cent through 2030 [3]. As the market expands, job opportunities also will likely increase.

How to become an analytics engineer

Although you can become an analytics engineer through other pathways, three steps might improve your chances. Consider earning a degree, gaining entry-level work experience, and building your skill set. 

1. Earn a degree.

Although it's not required for some positions, getting a bachelor's or master's degree can help you become an analytics engineer. Good fields to consider include:

• Data analytics engineering

• Computer science

• Data science

• Engineering

• Information systems

• Software development

• Mathematics

• Computational science

2. Gain entry-level work experience.

Working in an entry-level data job can help you gain valuable experience for an analytics engineer position. Examples of entry-level jobs you might consider include:

• Business intelligence analyst

• Data analyst

• Manufacturing analyst

• Operations analyst

• Quantitative analyst

3. Build your analytics engineer skills. 

As an analytics engineer, you can benefit from possessing a variety of skills. These include technical skills (which help you perform your job as an analytics engineer) and workplace skills (which have to do with how you work—either alone or when interacting with coworkers, employers, and stakeholders).

Technical skills

Analytics engineers rely on a variety of technical skills to perform their daily tasks and responsibilities. Technical skills an analytics engineer should possess include:

• Knowledge of data analysis

• Knowledge of data modelling and database management

• Experience with programming languages like Python and R

• Experience using cloud platforms like AWS and Azure

• Experience with data warehousing

• Experience using extract, transform, load (ETL) and extract, load, transform (ELT) tools

• Experience using data integration tools

Workplace skills

Because analytics engineers need to work well alone and with colleagues and company stakeholders, you'll also benefit from certain workplace skills. These include:

• An ability to problem-solve and work through data issues

• An ability to think critically

• Good communication and collaboration skills

• Good researching skills

• Leadership skills

• Knowledge of business

Taking extra classes or earning certifications can help give you extra skills for a job as an analytics engineer. On Coursera, you can find courses or certifications in applied business analytics, data engineering, data science, and more.

4. Familiarise yourself with tools used by analytics engineers.

A variety of tools help analytics engineers perform their jobs properly. Tools that can help you achieve success in this position include:

Data engineering and BI tools

Building data pipelines requires an array of different tools. These might include data warehousing tools like Google BigQuery or Snowflake, BI tools like Looker or Tableau, and ETL tools like Talend or AWS Glue. 

Python

Many people who work in data science use Python, a general programming language. As an analytics engineer, you might use Python for data mining, data modelling, data analysis, and data visualisation. 

SQL/data build tool (dbt)

SQL is the standard query language used to create and manipulate databases, so it's a tool that all analytic engineers use. Data build tool (dbt) relies on SQL to perform its functions. This important tool allows analytics engineers to transform data, test changes before deploying code, test data integrity, and more.

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Analytics engineers help companies understand and utilise their data for problem-solving, decision-making, and answering questions. This role involves collaborating with other data professionals to organise and analyse raw data, making it accessible and useful, ultimately driving better decisions, reducing costs, improving productivity, and increasing revenue.

To start building your skill set for a future analytics engineer career, consider earning your Google Data Analytics Professional Certificate on Coursera. You'll come away prepared to enter the workforce as a junior data analyst or gain a solid base of knowledge to ensure success when getting your degree. 

Just some of the topics covered in this course include data collection and cleaning, data analysis, and data visualisation, and you'll learn about tools used in the job, such as spreadsheets, SQL, and Tableau. The course takes just six months to complete at 10 hours per week, and you'll earn a Professional Certificate from Google when you're finished. 

professional certificate

IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

4.6

(78,119 ratings)

698,207 already enrolled

Beginner level

Learn More

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Generative AI, Data Science, Model Selection, Data Analysis, Python Programming, Data Visualization, Predictive Modelling, Numpy, Pandas, Dashboards and Charts, dash, Matplotlib, Cloud Databases, Relational Database Management System (RDBMS), SQL, Jupyter notebooks, Machine Learning, Clustering, regression, classification, SciPy and scikit-learn, CRISP-DM, Methodology, Data Mining, Github, Jupyter Notebook, K-Means Clustering, Data Science Methodology, Rstudio, Big Data, Deep Learning, Quering Databases, Data Generation, Career Development, Interviewing Skills, Job Preparation, Resume Building