In today’s data-driven world, businesses rely on the expertise of analytics engineers to optimize and streamline their data processes. Analytics engineers play a vital role in designing, building, and maintaining data systems that enable organizations to make informed decisions. In this article, we’ll explore the world of analytics engineering, including what it is, how to become an analytics engineer, the differences between data engineers and analytics engineers, the salary expectations, and more.
What is Analytics Engineering?
Analytics engineering is the process of designing and building data systems that allow organizations to collect, store, process, and analyze large volumes of data. Analytics engineers utilize various tools and technologies to ensure that data is organized, secure, and accessible for business intelligence purposes. They work closely with data scientists and business analysts to ensure data quality, develop data models, and create dashboards that provide actionable insights.
How to Become an Analytics Engineer?
To become an analytics engineer, you need to have a solid understanding of data engineering, database design, and data modeling. A degree in computer science, data science, or a related field is a good starting point. In addition, you’ll need to gain experience with data engineering tools such as Hadoop, Spark, and SQL, as well as business intelligence platforms such as Tableau and Power BI. You may also consider taking a data strategy course or a business intelligence certification to enhance your skills.
Is Analytics Engineering a Good Career?
The demand for analytics engineers is high, and the field is growing rapidly. As more companies rely on data to drive their decision-making, the need for analytics engineers will continue to increase. According to Glassdoor, the national average salary for an analytics engineer is $122,000 per year, with some earning over $150,000 per year. Additionally, the career offers ample opportunities for growth and advancement.
Data Engineer vs. Analytics Engineer
While data engineers and analytics engineers have similar skill sets, their roles are slightly different. Data engineers focus on the design and building of large-scale data infrastructure, while analytics engineers focus on the development and deployment of data analytics models. While both positions require a strong technical background, analytics engineers need to have more in-depth knowledge of statistics, data modeling, and business intelligence tools.
The Top Data Engineering Tools for Analytics Engineers
Analytics engineers rely on a range of data engineering tools to design and build data systems that enable effective data analysis. Here are some of the top data engineering tools that analytics engineers use:
Hadoop
Hadoop is an open-source software framework that is used to store and process large datasets. It provides a scalable, fault-tolerant platform for data storage and processing and is widely used in big data environments. Analytics engineers use Hadoop to build data lakes, which are large repositories of raw data that can be used for data analysis.
Spark
Apache Spark is an open-source data processing engine that is used to process large datasets quickly. It provides a scalable, distributed computing platform that can handle complex data processing tasks. Analytics engineers use Spark to build real-time data processing pipelines, which can handle large volumes of data in real-time.
SQL
Structured Query Language (SQL) is a programming language that is used to manage and manipulate relational databases. Analytics engineers use SQL to write queries that extract data from databases and to perform data transformations.
NoSQL
Not Only SQL (NoSQL) is a term used to describe databases that are not based on the traditional relational database model. NoSQL databases are designed to handle unstructured and semi-structured data, which makes them useful for big data environments. Analytics engineers use NoSQL databases such as MongoDB, Cassandra, and HBase to store and manage large volumes of unstructured data.
Business Intelligence Platforms
Business intelligence (BI) platforms such as Tableau and Power BI are used to visualize and analyze data. These platforms provide analytics engineers with tools for creating dashboards, reports, and data visualizations that can be used to extract insights from data.
Data Governance and Data Culture
Analytics engineers also play a critical role in data governance and data culture. Data governance ensures that data is managed effectively, including data quality, privacy, security, and compliance. Analytics engineers need to have a clear understanding of data governance and how to implement it effectively. In addition, they help create a data culture within the organization that promotes the effective use of data for decision-making.
The Importance of Data Governance for Analytics Engineers
Data governance is the process of managing the availability, usability, integrity, and security of data used in an organization. Analytics engineers play a critical role in data governance, as they are responsible for building and maintaining data systems that enable effective data analysis. Here are some of the key reasons why data governance is important for analytics engineers:
Ensuring Data Quality
Data quality is critical to effective data analysis. Analytics engineers need to ensure that the data they are working with is accurate, consistent, and complete. This requires a range of data quality checks and data cleaning processes.
Protecting Data Privacy
Analytics engineers need to ensure that data is protected from unauthorized access or use. This requires implementing data privacy controls such as access controls, data masking, and data encryption.
Ensuring Compliance
Organizations need to comply with various regulations related to data management, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Analytics engineers need to ensure that the data systems they build are compliant with these regulations.
Conclusion
Analytics engineering is a critical component of business intelligence and data strategy. As data becomes more integral to decision-making, the role of analytics engineers will continue to grow in importance. By building and maintaining data systems, creating data models, and developing business intelligence tools, analytics engineers enable organizations to make informed decisions that drive growth and success. If you’re interested in pursuing a career in analytics engineering, it’s important to have a strong technical background, be familiar with data engineering tools, and have a passion for working with data.
Justin is a full-time data leadership professional and a part-time blogger.
When he’s not writing articles for Data Driven Daily, Justin is a Head of Data Strategy at a large financial institution.
He has over 12 years’ experience in Banking and Financial Services, during which he has led large data engineering and business intelligence teams, managed cloud migration programs, and spearheaded regulatory change initiatives.