Big Data Engineer(s) work with a large array of complex datasets. As our world increasingly depends on these databases, their role is crucial in managing and handling data systems and tools. Let’s take a closer look at the role and job profile:
What does a Big Data Engineer do? Is the role different from a Data Engineer? The difference between the two roles can be unclear. The ambiguity over the role further increases when you take into account that the necessary skill sets are virtually the same.
In essence, the two titles are interchangeable and often reflect the same set of duties. A data engineer can be referred to as a big data engineer or big data architect.
What is Data Engineering?
Data Engineering is the management and processing of data. This includes:
- Development and construction of architecture systems
- Testing and maintenance of these systems
- Dealing with involving large-scale processing of data.
Essentially, Data Engineers work to maintain these systems of data processing.
With the world currently undergoing a digital revolution, data is now the fuel that drives forward the modern 21st century. Our lives revolve around huge data sets across varying fields and industries. These range from everyday sectors such as banking and education to e-commerce, and even healthcare. This has led to an abrupt rise in the way we use and manage databases.
The data mentioned here refers to a set of qualitative or quantitative variables – be it structured or not, digital or analogue, confidential or not. When you break it down, datasets are made up of individual data points that offer value.
The term Big Data does not mean more data. It refers to data points that accumulate at a much bigger rate than “normal” software can manage. The demarcation of big data is not strictly defined – but large amounts of data include:
- A million sales transactions by an online retailer
- A million hosted phone calls by a telecommunications provider
- A sensor that produces 50 megabytes of data every two nanoseconds.
And so if you work with and/or manage Big Data, you may be referred to as a Big Data Engineer. Due to the increased complexity of big data, the engineer has to learn multiple Big Data frameworks & NoSQL databases.
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Responsibilities of a Big Data Engineer
As a Big Data Engineer, you are responsible for the management of data. This includes utilizing the available data and technologies to create a data landscape for data scientists.
Your knowledge is not only limited to the data available in the company and its storage locations, but you are also responsible for data integration into central analysis infrastructure, and determining which technologies are suitable for this.
The work of a Data Engineer begins with understanding the technical requirements. They then move to plan and developing a robust and flexible big data infrastructure. They are responsible for collecting, storing, processing, and analyzing the data systems. A Big Data Engineer is considered the master of data supply. They make essential data easily accessible across the company and usable in multiple departments.
Big Data Responsibilities:
- Gather and process raw data at scale.
- Design and develop data applications using selected tools and frameworks.
- Read, extract, transform, stage, and load data to selected tools and frameworks as required and requested.
- Perform tasks such as writing scripts, web scraping, calling APIs, write SQL queries, etc.
- Work closely with the engineering team to integrate your work into our production systems.
- Process unstructured data into a form suitable for analysis.
- Analyze processed data.
- Support business decisions with ad hoc analysis as needed.
- Monitoring data performance and modifying infrastructure as needed.
- Define data retention policies.
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Big Data Engineer – Skills Required Required
As a Big Data Engineer, you’ll require certain skills. On the technical side of things, proficiency with Big Data Frameworks/Hadoop-based technologies is required. The Hadoop Ecosystem houses a number of different tools for different purposes.
Some essential tools which you need to master are:
- HDFS (Hadoop Distributed File System)
- PIG & HIVE
- Flume & Sqoop
Additionally, Database Architecture and design. Data Models & Data Schema are also amongst the key skills that a Data Engineer should possess.
Data Experts work closely with relational databases. It is important to know your way around SQL-based technologies such as MySQL and PL/SQL. Knowledge of databases such as Cassandra, MongoDB, and programming languages such as Python/R, is also essential. Furthermore, good teamwork and communication skills can help when working with team members.
What skills are required?
- Knowledge of data processes
- Skilled at Big data frameworks and Hadoop tools
- Knowledge of Database Architecture and Design
- Data Models and Data Schema
- Cross-divisional know-how
- Programming skills and SQL based technologies
- Talent in communication and teamwork
In general, when hiring a Data Expert, employees look for a Bachelor’s Degree in Computer Science, Software Engineering, IT, or a closely related field. Additionally, a certificate in Big Data can boost your visibility chances with a potential employer.
Big Data Certifications to consider:
- Google Professional Data Certificate
- Amazon Web Services (AWS) Certified Big Data – Specialty
- Cloudera Certified Professional (CCP) Data Engineer
- Data Science Council of America (DASCA) Associate Big Data
The average salary for a Big Data Engineer is around $103,000. The starting salary for junior engineers is around $72,000 and Senior engineers can expect to earn over $158,000 per year.
How much does a freelance big data engineer earn?
The average freelance hourly rate is $92. Extrapolated to an 8-hour day, the daily rate is around $736. (freelancermap price index – as of July 2020).