Data Analytics Engineer

A Data Analytics Engineer develops tools and processes to analyze large datasets, extracting valuable insights for informed decision-making.
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High-ROI Certifications
Potential Lateral Jobs
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Data Analytics Engineer

National (USA)
Base Salary
$103,717 / year
Additional Benefits
Bachelor's Degree

The average salary for Data Analytics Engineer is $103,717 / year according to

There are no updated reports for Data Analytics Engineer salaries. You can check potential lateral job opportunities in this information stack to find related salary information.

Data Analytics Engineer role may have an alternate title depending on the company. To find more information, you can check

Career Information

As a Data Analytics Engineer, you will be responsible for designing and implementing data analytics solutions for organizations. You will need a strong understanding of data analytics principles and technologies, as well as experience with programming languages like Python or SQL. Strong problem-solving and analytical skills are essential, as you will be responsible for optimizing data analytics performance and resolving any issues that arise.

The average salary for Data Analytics Engineer is $103,717 / year according to
AI Disclaimer
The following text about the Job role of Data Analytics Engineer has been generated by an AI model developed by Cohere. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.

A Data Analytics Engineer is a professional who specializes in analyzing and interpreting large sets of data to extract valuable insights and drive informed decision-making. They play a crucial role in helping organizations make sense of the vast amount of data they collect, enabling them to identify patterns, trends, and opportunities that can lead to improved business performance.

One of the most important skills for a Data Analytics Engineer is proficiency in programming languages such as Python, R, or SQL. These languages are essential for manipulating and analyzing data, as well as for building models and algorithms. Strong coding skills allow Data Analytics Engineers to clean and transform raw data into a format that can be easily analyzed and visualized.

Another crucial skill for a Data Analytics Engineer is a deep understanding of statistical analysis and data modeling techniques. They need to be able to apply statistical methods to identify correlations, make predictions, and uncover hidden patterns in the data. This requires knowledge of regression analysis, hypothesis testing, and machine learning algorithms.

Data Analytics Engineers also need to have a solid understanding of database management systems and data warehousing concepts. They should be able to design and optimize databases to store and retrieve large volumes of data efficiently. Additionally, they need to be familiar with data integration techniques to combine data from multiple sources and ensure data quality and consistency.

In addition to technical skills, Data Analytics Engineers should possess strong problem-solving and critical-thinking abilities. They need to be able to identify business problems and formulate data-driven solutions. They should also have excellent communication skills to effectively present their findings and insights to non-technical stakeholders.

The tasks of a Data Analytics Engineer can vary depending on the organization and industry. They may be responsible for collecting and cleaning data, performing statistical analysis, developing predictive models, creating data visualizations, and presenting insights to stakeholders. They may also collaborate with data scientists and business analysts to define project requirements and develop data-driven strategies.

Overall, a Data Analytics Engineer plays a vital role in helping organizations leverage their data to gain a competitive advantage. Their skills in programming, statistical analysis, and database management, combined with their problem-solving abilities, make them valuable assets in today's data-driven world.

Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.

High-ROI Programs

Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Certification Programs
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.

AWS Certified Data Analytics — Specialty


The AWS Data Analytics Certification program validates a deep understanding of AWS data analytics services and their integration with each other to derive insights from data, making it suitable for individuals pursuing a role focused on data analytics.

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Google Certified Professional Data Engineer

The Google Certified Professional Data Engineer is a high-ROI program designed for experienced professionals to become highly equipped with specialty skills and functional knowledge in cloud data engineering.

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Microsoft Certified: Azure Data Engineer Associate


The Microsoft Certified: Azure Data Engineer Associate program is designed for professionals who have expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems.

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AWS Certified Machine Learning — Specialty Certification


The AWS Certified Machine Learning - Specialty certification covers a wide range of topics, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations on the AWS Cloud. 

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Google Certified Professional Machine Learning Engineer

The Google Machine Learning Certification is a high-ROI program designed for ML engineers who want to gain specialized machine learning skills using Google Cloud technologies.

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Specialty Courses improving
If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.

Machine Learning Engineering for Production (MLOps) Specialization


This MLOps Specialization offers an in-depth understanding of creating, deploying, and maintaining integrated systems, managing data changes, and optimizing performance.

Data Engineer Career Track


This career track is designed for beginners to learn Python basics, build data architecture, streamline processing, and maintain large-scale systems. Participants will develop pipelines, automate tasks, and construct high-performance databases using Shell, SQL, and Scala.

Data Engineering for Data Scientists


This Nanodegree program teaches you how to access data silos, extract information from various sources, and streamline it into functional formats for analysts and high-level decision-makers. You will have the opportunity to create an impressive, machine learning-driven web application with significant real-world, life-saving implications.

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Become a Data Engineer

Career Track

This Nanodegree program equips learners with a strong foundation in Big Data Engineering. You will learn thoroughly about data models, data warehouses, data lakes, and data pipelines while working with massive datasets.

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Computational Probability and Inference


Mathematics for Machine Learning and Data Science Specialization


Introduction to Data Science


Deep Learning Specialization


IBM Data Analyst Professional Certificate


Genomic Data Science Specialization


IBM Data Warehouse Engineer Professional Certificate


Computer Vision for Engineering and Science Specialization


Image Processing for Engineering and Science Specialization


IBM Data Engineering Professional Certificate


Data Science with Databricks for Data Analysts Specialization


Resource Stacks

We are soon crowdsourcing these resource stacks to collate the best resources, such as publications, community groups, job boards, etc., that are practically suitable for every contextual stack.
Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Communities updating
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
Research updating
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.

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