If, like 92% of businesses out there, you’re increasing the rate of spend on big data analytics over the coming year, you need to consider incorporating data engineers into your team.

But what exactly do data engineers do?

Data engineers manage and organize your data so that it can be used effectively by employees that need it.

Traditionally, this would be data scientists and business analysts, but with big data becoming central to so many business functions, this could be a wide range of employees from different departments.

In other words, big data engineers are your ticket to well-structured, effective analytics across your business, whatever you’re using that for. With ‘data democratization’ rapidly becoming a huge priority for businesses, data engineers make it significantly more likely your employees can find, access and act on the masses of data your business collects.

As they’re so central to the success of your big data analytics investment, we’ve created this guide to explain:

• What a data engineer is
• What a data engineer does
• Key skills to look for in a data engineer
• Tools your data engineers will need
• How much a data engineer costs

What is a Data Engineer? 

Data engineering is all about designing, building and maintaining the systems that allow your organization to collect, store and use data at scale. 

A data engineer’s ultimate purpose is to transform raw data into a form that people across your organization – for example data scientists, business analysts and AI specialists – can use to evaluate and optimize performance. 

Data can then be used to: 

• Optimize marketing campaigns based on customer data
• Identify user experience improvements
• Develop new products and train AI/deep learning tools
• Create accurate forecasts and inform strategic decision making
• Identify areas for operational improvements

This gives your business a huge advantage when it comes to sales, product development and customer experience!

Data engineers achieve this by building and automating data pipelines which move data between systems. Often, a data pipeline will transform data into a usable format and load it into centralized repositories like data warehouses or data lakes for storage or processing. These Exact Transform Load (ETL) and Exact Load Transform (ELT) pipelines are central to analytics for business processes.

Data engineers are also responsible for creating or maintaining the data structures essential for data processing. These include:

• Relational and non-relational databases
• Data warehouses, data lakes and other large-scale repositories
• Servers for storage and processing

What Does a Data Engineer Do, Day to Day?

The data engineer role will vary across different organizations. No two data strategies are identical.

Consider what data you’re collecting and why you’re processing it. Are you using large amounts of data to train an AI, for example? Is your focus on customer analytics for more targeted and personalized marketing campaigns? Are you analyzing user data to fix pain points in your app’s
user journey?

Your data engineer’s day-to-day tasks will be informed by your priorities here. If you’re processing data for a range of reasons, their scope will be more varied. As a general rule, the following tasks are standard for data engineers wherever they work.

• Creating and maintain functional data pipelines
• Creating large, complex data sets for analysis
• Identifying opportunities for internal process improvements around data strategy
• Optimizing key data structures for scalability
• Creating data APIs
• Building the infrastructure needed for ETL or ELT processes
• Building analytics tools that other employees can access for insight into customer acquisition, operational efficiency, and other key business performance metrics
• Creating data tools for analytics and data scientist team members

Key Data Engineer Skills: What Should You Look For?

Coding: First and foremost, data engineers need to be at the top of their game in several programming languages. Exactly which ones will depend on the project, but there are a couple of absolute staples here.

Python: Python’s simple syntax, fast development speed and range of third-party libraries for data scientists make it an indispensable tool for coding ETL frameworks and APIs, automating pipelines, and carrying out major data wrangling tasks.

SQL: SQL (or Structured Query Language to give it its full title) is essential in managing data held in relational databases. Data engineers use SQL to execute queries to your databases, create business logic models and build reusable data structures.

Scala: Based on Java, Scala is super useful for general coding and stream processing (which allows you to query continuous data streams in real time). Major stream processing solution Apache Spark is based on Scala – if you’re using Spark, Scala is essential. Scala is such a useful skill that, according to Payscale, it’s associated with a 17% jump in earnings for data engineers that have it.

Relational and non relational databases

Databases are where you store your data so that it can be easily accessed, queried and updated. Depending on your requirements, databases can either be ‘relational’ (organized into tables which can be linked to others) or non relational (formatted in a different way).

How you structure your databases and the data you store inside them is central to a successful data strategy. It’s possible that your organization will need both types of database for various purposes, so data engineers should:

• Understand the advantages of each, and when to use them
• Have experience using major tools for relational and non relational databases (e.g. MySQL and PostgreSQL for relational databases, MongoDB for non relational).
• Be able to model relational databases to suit your business needs using data modeling techniques and schema.

What Tools do Data Engineers Use?

Alongside the programming languages mentioned above, there are several data engineer tools that are either essential for their job, or highly beneficial. Here are some absolute must-haves:

MySQL and PostgreSQL

PostgreSQL and MySQL are the most popular open-source relational databases out there. Each has a slightly different set of advantages and applications, so it’s worth finding a data engineer who’s experienced with both!

PostgreSQL is built on an object-relational model, whereas MySQL is traditional relational, for example. PostgreSQL works well with large datasets and offers high fault tolerance, but can be slower than MySQL depending on the circumstances.

MongoDB

If you’re looking to store lots of unstructured data, a noSQL database (also known as a ‘non relational’ database) is essential. MongoDB is a popular choice here – it’s open source with an extensive developer community, and packs in plenty of useful features. These include a distributed key-value store, document-oriented NoSQL capabilities, and MapReduce calculation capabilities.

Apache Spark (or other stream processing tools)

If you really want to become a data-driven organization, your employees need to be able to query streams of continuous data from multiple sources and act on the insights these suggest. This is called ‘stream processing’, and it’s essential for modern analytics practices.

You’ll need to choose a good stream processing tool to power this process. Apache Spark is a great option here. It’s widely used, open source and supports multiple programming languages – though to get the most out of it you’ll need expertise in Java/Scala.

Data warehousing tools

A data warehouse is a big, virtual repository for all your data, both current and historical. Once data enters your data warehouse, it can be transformed into usable, informative analytics for BI purposes.

There are lots of options here – many big tech vendors offer their own options, so you’ll find the offerings from SAP, Oracle, Microsoft and more. Other popular choices include Amazon Redshift, Google BigQuery and Snowflake.

How Much Does Hiring a Data Engineer Cost?

Data engineers are very much in demand right now, and that’s reflected in the salary you’ll need to offer for a viable in-house hire.

Data from Payscale puts the salary for an entry-level data engineer at $78,000. If you want a mid to senior level hire, expect to pay considerably over $100,000 depending on location.

You should also factor in:

• Hiring costs: these add up, particularly if you’re using external recruiters, who tend to take around 10-20% of first year salary as commission.
• Cost of tools and hardware: whilst many data engineering tools are open source, you will need to pay for some of them, as well as providing your engineers with the right hardware for the job.
• Benefits package: as data engineers are in demand, you will need to put a good offer down on the table to attract the best!

Does Your Data Engineer Need to Be In House?

Hiring full-time data engineers is expensive, and it will take time to find the right candidate for the job (even for in-demand hires, company/culture fit is important to get right).

If you see an in-house data engineering team as fundamental to your long-term success, you should absolutely think about making the investment sooner rather than later. It will give you more time to plan, build and establish the team and its role in your organization.

But what about if you need involved data engineering services immediately, or want more flexibility than an expensive in-house team offers?

Outsourcing your data engineering needs to a third party development agency like Tivix allows you to:

• Access a global network of data experts (so you’re not limited by locality)
• Scale a team as soon as your project starts, avoiding long in-house hiring processes
• Reduce overheads associated with in-house teams
  Only pay for the expertise you need, exactly when you need it

We have over a decade’s experience helping startups, nonprofits and Fortune 500 companies alike build a data strategy and implement the tools needed for it to succeed.

Get in touch today to discuss your data engineering needs