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Data Engineers

What is a Data Engineer?

Skills That Set Them Apart

A Data Engineer is highly skilled at focusing on concepts in order to:

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    Design distributed systems and data stores

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    Combine data sources

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    Create reliable pipelines for most recent data streams

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    Collaborate with Data Scientists and Data Analysts to build the right solutions

Three Levels of Data Engineering

Data Engineers generally fall into three levels of concentration.

01

Generalists:

They’re equipped to handle end-to-end data streams, such as cleaning, processing and analyzing. Although it’s the jack-of-all-trades version of a Data Engineer, it requires less system architecture knowledge is best suited forsmaller teams that don’t require as much data scaling.

02

Database specialists:

They’re tune-up experts, setting up data tables specifically for rapid analysis. These Data Engineers work at larger companies, where data comes in from a wide variety of sources. They are required to write scripts that are designed to merge this data to determine if there are further insights that may be gathered from heuristic combinations.

03

Pipeline specialists:

They’re skilled with medium-sized groups of information, curating the data to fit into specified formats used for analysis. Naturally, they understand the data architecture and are able to create algorithms to predict future consumer behaviors or trends.

The Educational Foundation That Sets the Stage

Specialized Training

Depending on the system architecture and requirements,
Data Engineers may go on to receive certification as:

Alteryx Certified Professional

to build visual data workflows and automated processes

IBM Certified Data Engineer

that focuses more on big data

KNIME L1 & L2 Proficiency Certification

for data visualization, exploration and data mining

Microsoft Certified Solutions Expert

with additional training to hone in on more granular content

ETL Tools

Extract, Transform, Load: ETL capabilities allow Data Wranglers to work seamlessly through this process. Some of the more popular platforms help corral both defined and fuzzy data from multiple sources. Stitch Data allows you to consolidate all of your data –even the information used for email, social media, live chat and SMS texts, and merge itwith quantitative data.

Segment captures, schematizes, and loads user data into your data warehouse of choice, tracks customer data and automatically sends it to a warehouse. This easy integration provides access to 200+ more tools on the Segment platform.

SQL-based Technologies

Extract, Transform, Load: ETL capabilities allow Data Wranglers to work seamlessly through this process. Some of the more popular platforms help corral both defined and fuzzy data from multiple sources. Stitch Data allows you to consolidate all of your data –even the information used for email, social media, live chat and SMS texts, and merge itwith quantitative data.

Segment captures, schematizes, and loads user data into your data warehouse of choice, tracks customer data and automatically sends it to a warehouse. This easy integration provides access to 200+ more tools on the Segment platform.

NoSQL-based Technologies

Extract, Transform, Load: ETL capabilities allow Data Wranglers to work seamlessly through this process. Some of the more popular platforms help corral both defined and fuzzy data from multiple sources. Stitch Data allows you to consolidate all of your data –even the information used for email, social media, live chat and SMS texts, and merge itwith quantitative data.

Segment captures, schematizes, and loads user data into your data warehouse of choice, tracks customer data and automatically sends it to a warehouse. This easy integration provides access to 200+ more tools on the Segment platform.

Expert Languages

Data Wranglers need to understand how each database architecture functions, i.e., howthe data is gathered, stored, retrieved, and then processed before they can select the appropriate tool.

More useful languages, therefore, are the ones that are the most versatile across multiple applications.

Java

Java is widely used because it has its own syntax, allowing programming to be written in English and then translated to numeric codes for the computer to understand.

Evolved from C/C++, this simpler language was created to ensure better reliability, enhanced security and easily transferrable between platforms. To be fluent in Java’s capabilities, a solid background in C/C++ comes in handy.

Python

Python is a relatively easy to learn software that is supported by an active community. Python has been gaining on R in popularity among Data Wranglers in recent years, though both of these open-source languages are popular.

Perl and Golang are especially helpful when retrieving very specific bits of data and Wranglers need to create simple, relatable programming that applies across multiple platforms.

Analytics

Operating Systems

Typical Data Engineers Compensation

How much does a Data Wrangler earn for their expertise? As expected, much of it depends on their specialty, credentials, experience and certifications. Be sure to read more about salaries, training and skillsets:

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Analytics Salary Increases When Changing Jobs

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Database Engineer Job Description, Duties and Requirements

What to Expect from a Wyzoo Data Engineer / Wrangler

Wyzoo Data Engineers work closely with Data Scientists and Data Architects to help filter data into meaningful streams of information that can be interpreted, analyzed and applied. They organize or wrangle specified and unspecified data so that it can be more readily combined with other relatable information from different sources.

They’re your team of experts who are responsible for:

  • Design, construct, install, test and maintain highly scalable data management systems
  • Ensure systems meet business requirements and industry practices
  • Build high-performance algorithms, prototypes, predictive models and proof of concepts
  • Research opportunities for data acquisition and new uses for existing data
  • Develop data set processes for data modeling, mining and production
  • Integrate new data management technologies and software engineering tools into existing structures
  • Create custom software components (e.g. specialized UDFs) and analytics applications
  • Employ a variety of languages and tools (e.g. scripting languages) to marry systems together
  • Install and update disaster recovery procedures
  • Recommend ways to improve data reliability, efficiency and quality
  • Collaborate with data architects, modelers and IT team members on project goals

Wyzoo’s Data Engineers conceive, build, maintain and improve your data analytics’ infrastructure by approaching data organization with a clear eye on your business goals,working with your business partners to help you target the right customers at the right time.