Data Science Gladiators, Lucrative Pipelines and The Automation Revolution

Businesses of the future invest in data pipeline innovations because the best solutions catalyze growth.  Data pipelines transport raw data from various data sources into data warehouses for analysis and business intelligence (BI). It takes a skilled group of professionals to manage the process correctly. Data science needs people with good business sense to combine data literacy and strategic thinking for solving problems. 

Data analysts, businesses analysts, and data architects contribute valuable insight and expertise to the data ecosystem. This article briefly highlights data engineers, data scientists, and machine learning engineers' roles and responsibilities. All of the titles listed and their associated roles unite to form a data driven organization.

Data Engineers

Data engineers are skilled at data plumbing. They are the builders who wear the hard hats at the construction site. These engineers repair, expand and tackle tasks that require knowledge of the nuts and bolts of maintaining a pipeline. It’s safe to say  They build pipelines and integrate them into the backbone, supporting high-performing ETL processes. Data engineers use various tools to help perfect the ETL process of Extract, Transform, and Load. ETL forms best practices and a structural guide for moving data throughout the data pipeline. In some cases, the organization's size influences whether the engineer, scientist, or analyst remains somewhat of a generalist or specializes in a particular set of functions within the ETL process.

Experience with API integration and data manipulation skills helps with the heavy lifting of bringing data into a staging area before transporting it to a data warehouse. However, all technical requirements can't drown out the essential components: the production blueprint for capturing, maintaining, processing, analyzing, and presenting data as it moves along the pipeline.  Data is continuously modified to make it more digestible and improve transportation efficiency.  Data engineers are the hard hats calling the shots to capture, extract, stage, and warehousing data. 

“The goal is to turn data into information, and information into insight.” Carly Fiorina

Data scientist 

The good ones are business savvy, steeped in analytics, and highly proficient at data visualization tools. They focus on the messaging apparatus after data leaves the warehouse and starts its journey down the pipeline. The data scientist converts data into actionable business intelligence. They are skilled at bridging the communication gap between computer programming and making business decisions. Presentation skills are paramount for translating then meshing the value from data mining and computer code into the organization's vision of competitive growth. Their research and recommendations offer guidance to the rest of the team. They build, explain, and advocate for the use of specific models in production. Python, R, SAS and SQLs are a few of the tools needed for bridging data engineers' efforts with recommendations and prototypes for the machine learning team.  

Machine Learning Engineer

These engineers navigate the software landscape, continually testing, training, and maintaining models they attach to the data pipeline. Machine learning engineers operate at the intersection of data science and software engineering. ML engineers also focus on scaling algorithms as the data and scope of business questions expand.  ML engineers determine which prototypes find a path into production. ML engineers are actively implementing algorithms to improve production output by using data to make forecasts, predictions and deliver a host of smart solutions at scale while avoiding production disruptions. 

Many of the algorithms require essential updates to function correctly. Implementing updates to important software libraries involves research and planning along with other related implementation duties. They understand the importance of conveying simple explanations for complex algorithms. This skill is necessary for communicating with decision-makers.

Businesses with a data-centric focus embrace change, juggle shifting roles, responsibilities, and data nuances in their business intelligence ecosystem. The ecosystem operates at an optimal level if the data science life cycle continuously acquires, preserves, processes, analyzes and communicates actionable business objectives. There are data-rich opportunities, and competition in this new big data arena is fierce, autonomous, and full of opportunities to discover a wealth of data-related opportunities.