Data Engineer

They’re Data Wranglers who are the architects of your data workflow. They manage incoming information and direct the process to interpret your data in accordance with your organization’s needs. They gather source data from the data scientists, and then turn it into a working model for more efficient analysis. Without the data science, there would be no need for data engineering; however both are equally important to the data mining process.

 

Skills That Set Them Apart

Data Engineers use gathered data and turn it into a more useful format, relative to the business requirements. They create data parameters that help identify and refine content within an established pipeline. They do this by being consummate problem-solvers who query multiple areas of information to get more specific details on what they’re measuring, while whittling away at what they’re not.

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

• Design distributed systems and data stores
• Combine data sources
• Create reliable pipelines for most recent data streams
• 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. 

• 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 for smaller teams that don’t require as much data scaling. 

• 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.  

• 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.

 

The Educational Foundation That Sets the Stage

Data Engineers select majors in Computer Science, Information Technology, Applied Mathematics, Engineering or any technical field. After graduation, they’ll move on to more specialized training. To be sure, Code is King. The ability to write code allows for arbitrary levels of abstractions and logical operation in a familiar way, integrates well with source control, is easy to version and to collaborate on. That’s why Data Wranglers are like coding cowboys.

Data Engineers are the builders of the data pipeline. They’re focused on providing the necessary infrastructure to support data generation. To do so, they orchestrate how the data comes to Data Scientists, by creating the scalable, high-performance framework that will help deliver clear business insights from raw data sources. Data Engineers will also implement processes that focus on data collection, management, analysis and visualization for real-time analytical solutions.

 

In order to get the training they need to handle specific requests and tasks, Data Engineers go on to receive more specialized training that’s tailored to their company’s systems. Depending on the system architecture and requirements, Data Engineers may go on to receive certification as:

• Google Certified Professional that provides a great background in data engineering
• IBM Certified Data Engineer that focuses more on big data
• CCP from Cloudera that provides analytics and ETL tool training
• Microsoft Certified Solutions Expert with additional training to hone in on more granular content

You can read more here.

 

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 it with 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

Versatility is what Data Engineers need in order to collect the exact information they need, which is why they start with the most universal languages. SQL (structured query language) is the backbone of complex queries and the industry standard among Data Engineers. PosgreSQL is one of the most advanced open-sourced relational databases in the world. Designed to run on UNIX-like platforms, as well as Mac OS, Solaris and Windows, it’s customizable in a variety of languages, such as C/C++ and Java.

 

NoSQL-based Technologies

Data warehousing may require some background in NoSQL, MongoDB or HBase. These systems work quickly with large volumes of data and are easily scalable for a more customized approach.

 

Expert Languages

Data Wranglers need to understand how each database architecture functions, i.e., how the 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 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 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

Data Wranglers build tools, infrastructure, frameworks and services. While they don’t analyze the data, they do provide the right pipeline for Data Analyzers to interpret and then develop actionable insights. Because they’re continuously improving the data’s path for it to be processed correctly, they need to be able to leverage different tools. A good data engineer saves a lot of time and effort for the rest of the organization by being well-versed in the Apache Hadoop platform, and know Hive or Pig as well.

 

Operating Systems

Depending on the business, servers and the parameters of the data requests, most Data Wranglers work in UNIX, Linux, OSX and Solaris. In general, the available data science tools have been developed from platforms that are most amenable to the creation and distribution of custom tools and programs. In most cases, this will be Linux.

How much does a Data Wrangler earn for their expertise?

Xperra 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

 

Xperra’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.