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big data engineer

What is a big data engineer?

A big data engineer is an information technology (IT) professional who is responsible for designing, building, testing and maintaining complex data processing systems that work with large data sets. This type of data specialist aggregates, cleanses, transforms and enriches different forms of data so that downstream data consumers -- such as business analysts and data scientists -- can systematically extract information.

A big data engineer is responsible for building and maintaining an organization's big data environment. This includes working on the big data architecture and technology, as well as data preparation and data management processes.

What is big data?

Big data describes massive volumes of customer, product and operational data, typically in the terabyte and petabyte ranges. Big data analytics is used to optimize key business and operational use cases, minimize compliance and regulatory risks, and create new revenue streams.

Possible sources of big data include the following:

  • Credit card and point-of-sale transactions.
  • E-commerce transactions.
  • Social media posts.
  • Smartphone and mobile device engagements.
  • Sensor readings generated by the internet of things.

There are a number of ways big data engineers get insights from big data analysis, including the following:

  • Optimizing key business and operations efforts.
  • Mitigating compliance and regulatory risks.
  • Identifying new revenue sources.
  • Building compelling, differentiated customer experiences.

What is the role of a big data engineer?

A big data engineer position encompasses many tasks, including the following:

  • Design, construct and maintain large-scale data processing systems that collect data from various structured and unstructured data sources.
  • Store data in a data warehouse or data lake repository.
  • Apply data processing transformations and algorithms to raw data to create predefined data structures. Deposit the results into a data warehouse or data lake for downstream processing.
  • Transform and integrate data into a scalable data repository or cloud.
  • Understand different data transformation tools, techniques and algorithms.
  • Implement technical processes and business logic to transform collected data into meaningful and valuable information. This data should meet the necessary quality, governance and compliance considerations for operational and business use. Knowledge of data quality management tools and frameworks can help with this.
  • Understand operations and management options, as well as the differences between data repository structures, massively parallel processing (MPP) databases and hybrid clouds.
  • Evaluate, compare and improve data pipelines. This includes design pattern innovation, data lifecycle design, data ontology alignment, annotated data sets and elastic search approaches.
  • Prepare automated data pipelines to transform and feed the data into development, quality assurance and production environments.
Diagram comparing the responsibilities of five big data jobs.
Several IT roles involve working with big data.

What are big data engineer skills and responsibilities?

Big data engineers gather, prepare and ingest their organizations' data into big data infrastructures. They prepare and create the data extraction processes and data pipelines that automate data from a wide variety of internal and public source systems. Big data engineers also create algorithms that transform the data into an operational or business format and have a range of problem-solving skills.

More specifically, big data engineer jobs require an understanding of the following:

  • Common data archetypes, writing and coding functions, algorithms, logic development, control flow, object-oriented programming languages, external libraries and how to collect data from different sources. This includes having knowledge of scraping, application program interfaces, databases and publicly available repositories.
  • Structured data, such as from relational database management systems, and spreadsheets; semistructured data, such as log files, Extensible Markup Language and JavaScript Object Notation; and unstructured data, such as text, video, audio and images.
  • Relational databases and NoSQL databases, such as Apache Hadoop, Apache Spark and other MPP databases.
  • SQL-based querying of databases using joins, aggregations and subqueries.
  • Open source tools, including real-time data processing products, such as Apache Beam, Kafka and Spark Structured Streaming; time series databases, such as InfluxDB; relational databases, such as Postgres; graph databases, such as Neo4j; and software development environments, such as Git and GitHub.
  • Abstraction tools, such as Kubernetes.
  • Mastery of computer programming and scripting languages, such as C, C++, Java and Python, as well as an ability to create programming and processing logic.
  • Experience with machine learning algorithms and automated machine learning to automate and build continuous learning data processing streams and pipelines.
  • Data warehousing tools and techniques, such as Apache Hive.

How does someone become a big data engineer?

A bachelor's degree in computer science, math or software engineering is the foundation for a successful big data engineer career. These courses of study teach concepts such as functional decomposition, logical thinking, problem resolution, solution engineering, abstraction and creating repeatable processes.

Big data engineer job descriptions usually require solid data processing experience and a willingness to learn new tools and techniques. Big data engineers must be willing to discard their current tool sets and embrace new, more powerful ones as they become available. They need to have a natural curiosity and a desire to learn about the continuously changing open source landscape.

Ideally, a prospective big data engineer has hands-on experience with business intelligence, data modeling and data warehousing, as well as data science and data lake projects.

IT professionals also must have strong communication skills to fill a big data engineer role. Their skill set must include the ability to collaborate with business subject matter experts, business analysts and data scientists. Through such collaboration, data engineers are able to identify, validate, value and prioritize business and operational requirements.

There are a number of certifications data engineers and architects should consider to improve their skills. Certifications measure a candidate's expertise against industry benchmarks to show prospective employers that you have what it takes to succeed. They include courses such as the following:

  • Cloudera Certified Professional Data Engineer.
  • Databricks Certified Data Engineer Professional.
  • Google Cloud Certified Professional Data Engineer.
  • IBM Data Engineering Professional Certificate.

What are typical big data engineer salaries?

Big data engineer salaries are at the higher end of the IT pay scale. According to Glassdoor, the average salary for this job in the U.S. is about $106,000 per year; base pay ranges from $90,000 to $126,000 per year.

Big data engineer salaries are comparable to those of other data professionals, such as data analysts or data architects. For example, Glassdoor pegs the average salary for U.S. data architects at about $139,000 per year, while Salary.com estimates the average data analyst's annual salary to be about $85,000. These and other related in-demand jobs require technical skills that not many people possess, therefore they command high compensation that increases significantly with years of experience.

DataOps is a growing discipline that involves building and maintaining data architectures to create business value from big data. Find out why organizations consider DataOps as a way to improve data use.

This was last updated in January 2024

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