Data Engineer

Data engineers build and maintain data systems. They construct datasets that are easy to analyze and support company requirements.Data engineers implement methods to improve data reliability and quality. They combine raw information from different sources to create consistent and machine-readable formats. They also develop and test architectures that enable data extraction and transformation for predictive or prescriptive modeling.

Data Engineers are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data. Then, they write complex queries on that, make sure it is easily accessible, works smoothly, and their goal is optimizing the performance of their company’s big data ecosystem.

They might also run some ETL (Extract, Transform and Load) on top of big datasets and create big data warehouses that can be used for reporting or analysis by data scientists. Beyond that, because Data Engineers focus more on the design and architecture, they are typically not expected to know any machine learning or analytics for big data.

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  • Analyzing raw data.
  • Developing and maintaining datasets.
  • Improving data quality and efficiency.

These responsibilities may vary based on each company’s needs. When crafting your own data engineer job description, make sure to tailor them accordingly.

Responsibilities

  • Analyze and organize raw data
  • Build data systems and pipelines
  • Evaluate business needs and objectives
  • Interpret trends and patterns
  • Conduct complex data analysis and report on results
  • Prepare data for prescriptive and predictive modeling
  • Build algorithms and prototypes
  • Combine raw information from different sources
  • Explore ways to enhance data quality and reliability
  • Identify opportunities for data acquisition
  • Develop analytical tools and programs
  • Collaborate with data scientists and architects on several projects

Requirements

  • Previous experience as a data engineer or in a similar role

    Technical expertise with data models, data mining, and segmentation techniques
  • Knowledge of programming languages (e.g. Java and Python)
  • Hands-on experience with SQL database design
  • Great numerical and analytical skills
  • Degree in Computer Science, IT, or similar field; a Master’s is a plus
  • Data engineering certification (e.g IBM Certified Data Engineer) is a plus

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Data engineers build massive reservoirs for data and are key in managing those reservoirs as well as the data churned out by our digital activities. They develop, construct, test, and maintain data-storing architecture — like databases and large-scale data processing systems. Much like constructing a physical building, a big data engineer installs continuous pipelines that run to and from huge pools of filtered information from which data scientists can pull relevant data sets for their analyses.

Data engineers typically have an undergraduate degree in math, science, or a business-related field. The expertise gained from this kind of degree allows them to use programming languages to mine and query data, and in some cases use big data SQL engines. Depending on their job or industry, most data engineers get their first entry-level job after earning their bachelor’s degrees. Here are five steps to keep in mind if you are planning on becoming a data engineer:

Data engineering is a highly variable, big-tent field with a primary focus on developing reliable mechanisms or infrastructure for data collection. A data engineer essentially is anyone who serves as a gatekeeper and facilitator for the movement and storage of data. Data engineers are also often tasked with transforming big data into a useful form for analysis. In order to do this, the design, construct, install, test, and maintain highly scalable data management systems — basically, software needed to store and use this data.

Data engineers should also have an understanding of other programming languages that help with statistical analysis and modelings, such as Python or R. A mastery of Spark, Hadoop, and Kafka will come in handy, too.

Data engineering is a highly strategic job with many responsibilities spanning from the construction of high-performance algorithms, predictive models, and proof of concepts, to developing data set processes needed for data modeling and mining.

Here is an overview of data engineer responsibilities:

  • Ensuring that data storage and collection systems meet business requirements and accepted industry standards.
  • Integrating new data management software into a company’s existing structures or research new opportunities for a business’ data acquisition. This could mean helping a company come up with a new way to efficiently bring in data from a brand-new client.
  • Creating custom software components using a wide range of languages and tools — like scripting languages — to merge different systems together or develop a strong analytics infrastructure for measuring your data stored by a business.
  • Storing and processing data securely at all times. Data engineers remain on the frontlines of a company’s cyber defenses, installing and updating disaster recovery protocols, in addition to recommending ways to improve data reliability and quality.
  • Becoming a data engineer can be an opportunity to collaborate with an interdisciplinary group of people, working closely with data architects, modelers, and IT specialists to achieve different project goals.

The data engineering field is one that is constantly evolving. Shifts in the industry like the evolution of Hadoop, which is increasingly being used as an enterprise data hub, advances in processing power for predictive analytics, and a general move toward the Cloud could make a data engineer’s life more complicated. But it also presents more job opportunities.You can work as a data engineer, a senior cloud data engineer, a senior data engineer, and a big data engineer, among other roles.

Basically, it’s an exciting time to be a data “builder.” If you love playing with new tools and can think outside the relational database box, you’ll be in a prime position to help companies adapt to the demands of this industry.

Data Engineers are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data. Then, they write complex queries on that, make sure it is easily accessible, works smoothly, and their goal is optimizing the performance of their company’s big data ecosystem.

They might also run some ETL (Extract, Transform and Load) on top of big datasets and create big data warehouses that can be used for reporting or analysis by data scientists. Beyond that, because Data Engineers focus more on the design and architecture, they are typically not expected to know any machine learning or analytics for big data.

Skills: Hadoop, MapReduce, Hive, Pig, Data streaming, NoSQL, SQL, programming.
Tools: DashDB, MySQL, MongoDB, Cassandra


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