Data engineers are essential members of any enterprise data analytics team, as they are in charge of managing, optimising, supervising, and monitoring data retrieval, storage, and distribution throughout the organisation. In the article, we will explain Data Engineer Job Description.
What is a Data Engineer?
Data engineers are in charge of identifying trends in data sets and developing algorithms to help make raw data more useful to businesses. This IT position necessitates a diverse set of technical skills, including in-depth knowledge of SQL database design and multiple programming languages.
However, data engineers must be able to communicate across departments in order to understand what business leaders want to gain from the company’s large datasets.
Data engineers are frequently in charge of developing algorithms to facilitate access to raw data, but in order to do so, they must first understand the company’s or client’s objectives. When working with data, it’s critical to align business goals, especially for companies that deal with large and complex datasets and databases.
Data engineers must also know how to optimise data retrieval and create dashboards, reports, and other visualisations for stakeholders. Data engineers may also be in charge of communicating data trends, depending on the organisation.
Larger organisations frequently employ multiple data analysts or scientists to assist with data interpretation, whereas smaller businesses may rely on a data engineer to fill both roles.
The Data Engineer role
According to Dataquest, data engineers can be classified into three categories. These are some examples:
Generalist: Generalists are commonly found on small teams or in small businesses. As one of the few “data-focused” people in the company, data engineers wear many hats in this environment. Generalists are frequently in charge of all aspects of the data process, from data management to data analysis. According to Dataquest, this is a good role for anyone looking to move from data science to data engineering because smaller businesses won’t have to worry as much about engineering “for scale.”
Pipeline-centric: Pipeline-centric data engineers are frequently found in midsize companies, where they collaborate with data scientists to help make use of the data they collect. According to Dataquest, pipeline-centric data engineers must have “in-depth knowledge of distributed systems and computer science.”
Database-centric: Data engineers focus on analytics databases in larger organisations where managing the flow of data is a full-time job. Database-centric data engineers create table schemas and work with data warehouses across multiple databases.
Data Engineer Duties and Responsibilities
A Data Engineer’s typical duties and responsibilities may include: In addition to creating and maintaining an optimal pipeline architecture, a Data Engineer’s typical duties and responsibilities may include:
- Assembling large, complex sets of data to meet non-functional and functional business requirements
- Identifying, designing, and implementing internal process improvements, such as re-designing infrastructure for increased scalability, optimising data delivery, and automating manual processes.
- Creating the infrastructure required for optimal data extraction, transformation, and loading from various data sources using AWS and SQL technologies.
- Developing analytical tools to take advantage of the data pipeline, providing actionable insight into key business performance metrics such as operational efficiency and customer acquisition.
- Working with stakeholders such as data, design, product, and executive teams, as well as assisting them with data-related technical issues.
- Working with stakeholders such as the Executive, Product, Data, and Design teams to support their data infrastructure requirements while also assisting with data-related technical issues.
Data Engineer Skills and Qualifications
A Data Engineer job description should include the following skills and qualifications:
- Capability to create and optimise data sets, “big data” data pipelines, and architectures.
- Ability to conduct root cause analysis on external and internal processes and data in order to identify opportunities for improvement and provide answers.
- Excellent analytic skills required for working with unstructured datasets.
- The ability to create processes that support data transformation, workload management, data structures, dependency, and metadata.
Data Engineer Salaries
The average salary for a data engineer is $137,776 per year, with a reported salary range of $110,000 to $155,000 depending on skills, experience, and location, according to Glassdoor. Senior data engineers earn an average annual salary of $172,603, with reported salary ranges ranging from $152,000 to $194,000.
According to Glassdoor, the following are the average pay rates for data engineers at some of the top tech companies:
- Amazon: Reported salary range ($78,000 – $133,000) and Average annual salary ($103,849)
- Hewlett-Packard: Reported salary range ($64,000 – $105,000) and Average annual salary ($86,164)
- Hewlett-Packard: Reported salary range ($93,000 – $171,000) and Average annual salary ($122,695)
- IBM: Reported salary range ($90,000 – $116,000) and Average annual salary ($99,351)
Data Engineer Education and Training Requirements
A Data Engineer position typically necessitates a combination of educational requirements, beginning with a bachelor’s degree in information technology or computer science and continuing with additional vendor-specific certification.
Google’s Certified Professional-Data-Engineer certification proves that a person is familiar with data engineering principles and can work as an associate or professional in the industry.
Many in the industry regard the IBM Certified Data Engineer – Big Data certification as a gold standard because it focuses on big data-specific applications rather than general skills.
The CCP Data Engineer for Cloudera certification, which is specific to Cloudera’s solutions, demonstrates the individual’s proven experience in ETL analytics and tools.
Secondary certifications, such as the Microsoft Certified Solutions Expert (MCSE), cover a wide range of topics and include specific sub-certifications such as MCSE: Data Management and Analytics.