Typical Data Engineer Job Description

Big data and data analytics have become mainstream technologies. They have become the driving force behind the many innovations that have come up as solutions to extracting insights and information out of the vast volumes of data being generated every passing day. Big data has penetrated all industries so much that it is now the lifeline not only for businesses but also for professionals who deal with data.

Owing to this, the data engineering role has become an all too important role in organizations. Without data engineers, these organizations will not have the proper infrastructure and data management technologies to effectively make sense of the huge volumes of data that they have access to. With businesses undergoing a digital transformation that has brought about a sharp demand for data engineers, it is high time professionals considered enrolling in a data engineer course to upskill.

The data engineering role

Data engineers are the individuals responsible for discovering trends and patterns in data sets, extracting useful information and insight from the data sets, and developing the infrastructure required to transform volumes of raw data into formats that can be manipulated to extract valuable insight.

Apart from their ability to crunch numbers, data engineers should possess a number of technical and interpersonal skills. Also, the skills requirements for this role depending on the level of the data engineering position. This could be:

Junior, mid-level, and senior data engineer roles or generalist, database, or pipeline data engineer.

These roles are determined by one’s years of experience, skill level, and qualifications.

Typical Data Engineer Job Description

The major responsibility of a data engineer is to build and maintain data infrastructure and systems. The individual should have the technical ability to optimize the data pipeline of the organization to facilitate efficient data flow to cross-functional teams in the organization.

Roles of the data engineer

• Design, build and maintain data analytics infrastructure based on the requirements of the organization. These include architectures, servers, database systems, and other data processing systems.
• Analyze and organize raw data into datasets that data analysts or scientists work with in analyzing data.
• Explore opportunities for data acquisition
• Develop techniques for improving data quality and reliability
• Prepare data for modeling
• Develop algorithms
• Support software developers, data scientists, data architects, and data analysts by maintaining data architecture throughout data projects that are in progress.
• Analyze the company’s data requirements and design effective solutions
• Perform complex data analysis, report results, and interpret trends and patterns
• Build data analytics tools to be used by the data analytics and data science teams to draw actionable insights to increase operational efficiency, increase customer satisfaction, and achieve other key business objectives.
• Build or improve data transformation, data structure, and ETL processes

Key skills

The data engineering role will require the following sets of technical and interpersonal skills.

Technical skills

• Programming languages like Python, Java, and Scala
• Data warehouse knowledge
• Data analysis
• SQL and NoSQL database and distributed systems knowledge
• Big data processing frameworks like Apache Spark and Hadoop
• ETL
• Linux
• AWS technologies
• Data wrangling
• Data modeling
• Machine learning
• Project management and organization skills

Soft-skills

• Excellent communication skills
• Collaboration skills
• Presentation skills
• Detail-oriented
• Self-directed
• Ability to work in a multiple-team setting and support the data requirements for all the teams and the business as a whole

Knowledge and Experience

• Experience designing and building data pipelines, structures, and datasets
• Advanced SQL knowledge
• Working knowledge of various relational and nonrelational databases
• Solid background working with unstructured datasets
• Data modeling, mining, and segmentation knowledge
• Working knowledge of programming languages
• Experience carrying out root cause analysis for business processes to identify business requirements or opportunities for improvement
• Stream processing, message queueing, real-time data analysis experience

Tools and technologies

• Data pipeline tools like Luigi and Airflow
• Big data tools like Hadoop, Apache Spark, and Apache Kafka
• Relational SQL and NoSQL databases like Cassandra as well as non-relational databases
• AWS cloud products like EMR, RDS, and EC2
• Stream processing systems like Spark-streaming
• Programming languages Python, Java, Scala, C++
• Linux operating system

Training and qualifications

• Bachelor’s degree in computer science, IT, information systems, Computer engineering, or related field (Master’s will be an added advantage)
• Certification in data engineering e.g IBM Certified data engineer certification or Google Cloud Certified Professional Data Engineer

Salary prospects

According to Indeed, data engineers earn an average annual salary of $127,983. According to Payscale, an entry-level data engineer will earn an annual average salary of $77,361 alongside bonuses and allowances. A data engineer’s salary depends on a number of factors, including:

• Qualifications
• Years of experience
• Location
• Size of the hiring organization

Conclusion

The big data job description we have given is a general description that rounds up the common roles, skills, qualifications, and experience of a data engineer. The specific roles and responsibilities of the data engineer depend on the domain he/she will be working in, the size of the company’s IT department, and the organizational structure of the organization, among other factors. Bottom line, whether at entry or senior level, the data engineer attracts quite a decent pay package; thus choosing to pursue a career in data engineering can be very rewarding. Again, it is fulfilling because one becomes the professional that the organization counts on to develop working data-oriented solutions ultimately driving business growth, profitability, and customer acquisition.