what is a data engineer
According to MIT Sloan School of Management, data engineers collect, manage and administer data. They are a critical part of any data operation by creating the architecture for acquiring and processing raw data and then preparing it for data scientists to analyze this information and create insights from it. Data engineers identify trends in data sets and develop algorithms as part of the prep work. Like many IT roles, data engineers possess very deep and specific technical skills, such as SQL database design, multiple programming languages and cloud services.
In addition to the need for technical skills, they are part of a team that must deliver critical insights needed by business leaders to guide their day-to-day and long-term strategic goals. By enabling these executives to quickly understand and react to immediate and emerging trends, analytics teams play an important role in facilitating outcomes for their organizations.
frequently asked questions
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Do data engineers perform similar work as data scientists?
Not exactly. Engineers focus on making sure the information that will be used to create business insight is accurate, clean and ready for use by data scientists. These two roles may work closely together to ensure the analytical work results in information that business leaders can understand and use to achieve their business goals.
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Will I be able to get a job right away as a data engineer after graduating from college?
Many employers look for candidates with at least a few years of work experience in the field, but with a shortage of data engineers right now, some are recruiting graduates that have strong programming and technology knowledge and problem-solving skills. The best way to get work as a data engineer is to acquire many of the base skills and build on them through additional certification and working on data projects.
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Aren’t data engineers simply a subset of computer coders?
Coding is an essential skill that data engineers must possess, but their work is far more complex than just programming. An understanding of data architecture, databases and distributed systems is required. They must be able to identify issues with data sets, develop solutions to address them and integrate the data into the systems that will be used to analyze the numbers.
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Is a master’s degree in data engineering required to advance in this field?
Not all companies require their data engineers and scientists to have a master’s, but to acquire a management-level role, it is strongly recommended. There are many strong data experts who work in the field without a postgraduate degree, leveraging their work experience and technology expertise to get ahead in their field. However, a master’s or Ph.D. offers greater understanding about theories and problem-solving. Additionally, certification in various tools and technologies can also help advance a career in this field.
average salary of a data engineer
According to LinkedIn’s 2020 Emerging Jobs Report, data engineer is ranked 8th among the top 15 jobs in the U.S., with an annual growth of 33%. According to Dice, data engineers in the U.S. are paid an average salary of $113,240, a growth of 9.3% in 2019, driven in part by a 50% increase in job postings.
working as a data engineer
types of data engineers
Data engineers typically fall into one of three types: generalists (oversees all data tasks within an organization including analytics), pipeline-centric (manages all the data flow into the company) and database-centric (works with multiple databases). The size of the organization often dictates the type of data engineer employed since smaller ones may be limited to a small team or even just one individual managing the data. Companies with more resources may be able to deploy more engineers to support a higher volume and broader analytical needs.
daily work routine
From one day to the next, data engineers work with business and IT colleagues to develop architecture and create interfaces (APIs) that improve the usability of data. Whether they are preparing the information for use in a dashboard, to be imported into a database or extracted for other purposes, the engineer is responsible for ensuring the integrity of the data and pipelines. Other regular tasks include combining different data sets, determining how to store the information and working with data scientists and analysts to acquire the needed insights.
work environment
While they do work within a team, data engineers can perform their jobs on-site or remotely. The tools and datasets utilized for the job are all digital so there are no limitations to where they physically sit as long as they have secure access to their servers. Only company culture and policies dictate whether the work is performed on-site or virtually, but considering the current broad adoption of working from home, many data engineers are likely to continue to perform their duties remotely.
duties and responsibilities
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data engineer's duties
The daily tasks involved in achieving these goals are varied. These include:
- Extracting data and preparing it as part of the ETL (extract, transform, and load) processes
- Converging data sets
- Evaluating, parsing, and cleaning data sets
- Coding and executing
- Creating data stores and utilizing these for analysis
- Using frameworks to serve data
It is the data engineer’s main responsibility to ensure the information made available to scientists and other stakeholders is true and usable. This also requires close collaboration with other team members including application developers, data scientists and database administrators.
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data engineer's responsibilities
According to the University of Virginia’s School of Data Science, the main responsibilities of a data engineer include:
- Developing, constructing and maintaining databases, architectures and pipelines
- Building architectures to support data scientists’ analysis capabilities and meet business needs
- Presenting innovative ways to collect useful data
- Creating data modeling and mining processes
- Recommending ways to improve data quality and reliability
- Finding data efficiency opportunities
work schedule
pressure on data teams
With so many companies generating massive amounts of data and accelerating their digital operations, the need for business insights has never been greater. This is putting tremendous pressure on data teams to rapidly collect, extract and process information more expeditiously.
length of workday
For data engineers, this can mean long days behind the desk as they face more projects. For generalists that work at small and mid-sized companies, they may be asked to work long hours to meet growing demands. The hours are dictated by a number of factors, including company culture, type of business, staff size and growth trajectory.
opportunity for a variety of projects
Increasingly, companies are deploying data engineers on a contingent or contract basis to meet their growing data needs. This allows some workers to take on various projects and gain valuable experience in different technologies to meet a variety of business needs. These arrangements also allow non-permanently hired data engineers to move from one client to another to gain more exposure to new challenges and opportunities.
education and skills
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education & qualifications
To pursue a career in data engineering, key skills involving programming, mathematics, software development, data mining, database management, IT and cybersecurity. Having a strong technology background is required of all types of data engineer, whether the role is a generalist, a pipeline-centric engineer or a database-centric expert. Most organizations hiring data engineers look for candidates with the following degrees:
A bachelor’s, master’s or Ph.D. in:
- Mathematics
- information technology
- computer science
- software engineering
In addition to a university education, employers may look for certification in one of several key technology areas. According to CIO, the following are the most sought-after certifications for data engineer and architect. These include:
- Amazon Web Services (AWS) Certified Data Analytics – Specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP): Data Engineer
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Data Science Council of America (DASCA) Senior Big Data Engineer
- Google Professional Data Engineer
- IBM Certified Data Architect – Big Data
- IBM Certified Data Engineer – Big Data
- SAS Certified Big Data Professional
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skills and competencies
Data engineers need to be well-skilled in data architecture and database design and maintenance. To competently perform their jobs, they are required to have strong knowledge of a variety of technologies and languages – as many as 10 to 30 to choose the best tools for the projects they work on. Many organizations often will deploy a single suite of cloud services from one vendor, so having a deep understanding of one platform is often necessary, whether that’s on AWS or Azure.
Some of the skills a data engineer needs include:
- Apache Spark
- SQL
- Hadoop
- Beam
- Java
- Python
- R
- Kafka
- Extract/Transform/Load (ETL)
- Amazon Web Services
- Databases
- Shell scripting
- Distributed ML Platforms: MLib (Spark)
- Parallel Computing for Deep Learning (Tensorflow, GPU Programming)
- Development in Containers (Docker, Rkt)
- Programming in Notebooks (Zeppelin, Jupyter)
- Java, C++, and/or Go and functional languages (Scala, Clojure, Elixir)
Beyond technical skills, career advancement also requires many soft skills typically possessed by managers in any function: strong communication, team-oriented collaboration, good project management and efficient use of time. Because data engineers are typically asked to fulfill a business need, they must be able to work with a number of data colleagues and operational leaders to determine the objective of any project or initiative.
job outlook
boundless opportunities
As one of the highest in-demand roles in the world, a career in data engineering is expected to offer boundless opportunities due to rising demand for the foreseeable. According to Bain & Company, the global advanced analytics talent pool, which includes data engineers among other roles, will reach one million. This number has doubled since 2018. Even so, the consulting firm also predicts that in the U.S., a shortage of data engineers may remain, despite an anticipated surge among the ranks of data scientists.
increasing demand
According to the U.S. Bureau of Labor Statistics, the job outlook for computer and information research scientists (a profession in which data engineers are grouped) is growing at a much faster rate than most jobs – a 15% increase is expected from 2019 to 2029. In the EU, one study estimates the market there faces a shortage of nearly a half-million data workers in 2020.
top tech job
Dice in 2019 reported that data engineer employment postings rose 88.3% in 12 months and remained the top tech job. With the pandemic forcing many companies to accelerate their digital transformation, demand for data professionals are likely to witness further double-digit growth in the months and years ahead.
similar job roles
work for randstad
advantages to work for randstad as a data engineer
As the largest HR services business in the world, Randstad works with some of the most experienced and talented data engineers and leading companies that employ them. As a provider of talent to most of the Fortune 500 companies, our candidates have access to the most admired businesses in their field, including leading IT&C companies, life sciences, financial services, manufacturing and others.
Randstad ‘s experienced recruitment teams around the world leverage the latest talent technologies to create strong matches of candidates to available job openings. Our recruiters also spend significant one-on-one time with job seekers to understand their professional desires and connect them with the right employers.
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