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3 min read

Data Engineers vs Data Scientists – What’s the Difference?

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So, you’re a data engineer. Or you’re a data scientist. Or you might consider yourself to be both, but you have one title at work, and there’s always that one person who messes up and introduces you as, “Our data scient… err, data engine… err, data person.” You used to correct them, but that grew tiring. At TimeXtender, we understand the necessity of labels in certain circumstances, but we also know that you take pride in your job as a data engineer or data scientist. 

Data engineers and data scientists both work with data on a daily basis, but there are some distinct differences in their roles and tasks. In most cases:

Data Engineers:

  • Design, build, maintain, and troubleshoot the infrastructure to store, process, and analyze data
  • Ensure the efficiency, scalability, and reliability of data pipelines
  • Work with data storage technologies such as databases and data warehouses

Data Scientists: 

  • Clean, process, and analyze large sets of data to find insights and drive business decisions
  • Develop predictive models and algorithms using statistical and machine learning methods
  • Communicate findings and insights to stakeholders through visualizations and presentation

Data engineers tend to focus on the technical aspects of handling data, while data scientists focus on using the data to generate insights and inform business decisions. Each role is important within a data team. But how do you define or separate these roles, or even decide if they should be separated?

Which One Are You?

As a data engineer or data scientist, you most likely feel confident in which one you are, even if you straddle the line when it comes to data responsibilities. Most of you are probably doing all kinds of tasks, including data processing, data warehouse building, data modeling, and more. While it might be important to define your title when it comes to the workplace, you may not want to put a label on what you do and who you are (it’s just a job/career title, after all). At TimeXtender, we believe that those who love data should be able to work with it however they need or want, whether that’s building a comprehensive data lake from various sources or updating predictive models through machine learning, so your business intelligence (BI) analysts have better and more up-to-date information to take to shareholders and business owners. This is why we’ve built our holistic data integration tool – to help with data warehouse automation, extract-transform-load (ETL) tasks, semantic layer modeling, and more.

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Which Should You Hire?

If you’re a business owner or data team manager, you might be looking to hire a data engineer, a data scientist, or both. It’s important to understand what type of data expert you need, as you likely have a limited budget with which to hire and train a new data specialist. 

Let's say you run a logistics and supply chain business that has three international production centers where products and systems are made to protect cargo carried by rail, ship, truck, and air. Your day-to-day operations involve several adjustments and changes based on customer demand and market trends, which means your data team needs to be agile and flexible so the most important data can be ingested, prepared, and delivered as quickly as possible. A data engineer can build and maintain the data warehouse, while a data scientist can analyze the data to find the appropriate insights for future business decisions. Just like Cordstrap, you need data delivered on time and within budget, so who do you hire – the data engineer or the data scientist? With TimeXtender, either role can find the data agility and holistic integration they need for data warehouse automation, business intelligence tools, and more. The roles of engineer and scientist can essentially be merged into one powerful mega-role, where nothing stands in the way of creating clean, usable data.

There’s also the opportunity to create a completely new role and title, such as that of an “analytics engineer.” This role can combine the responsibilities of a data engineer and data scientist, where building and maintaining a healthy data warehouse is just as important as cleaning and process data for business insights. This type of role can be especially powerful for smaller businesses where budgets and resources are limited, and it’s not possible to hire two people for the data team. Check out this video from Joseph Treadwell, our Head of Partner & Customer Success, on how an analytics engineer can change how small businesses use and process their data.

What Really Matters

In the end, you can have the title of data engineer, data scientist, or analytics engineer, but what really matters is how you approach and use data for creating better results and better outcomes. At TimeXtender, we’re all about giving data specialists time back in their day through our holistic approach that allows you to build data solutions 10x faster. No more fragmented tools that drive up costs and suck away all your time – you’ll have one tool with core features such as end-to-end orchestration, data lineage documentation, and more. Why not try it now?

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