In a world where big data is driving huge value for organizations across many industries, Data Engineers are in demand. The recruitment rate of Data Engineers shows a 33% annual increase according to last year’s Emerging Jobs Report. This blog explains what Data Engineers do, what their vital qualities are, and why outsourcing Data Engineering projects is the way forward for many enterprises.
The Data Engineer’s Role
A Data Engineer effectively completes all the groundwork that enables BI, Analytics and Data Science to work and provide value. Key responsibilities include:
- Data ETL – moving data to a unified repository – typically a data warehouse
- Data pipelines – to clean and prepare raw data, apply transformations, and provide infrastructure to draw meaningful insights
- Data collection – obtaining and integrating data sets for an organization
- Data algorithms – to facilitate data science (BI, data analytics, machine learning and so forth)
- Data architecture design – to create a data-driven organization
4 Essential Data Engineer Qualities
Development Skills
Strong development skills are a must for successful Data Engineers. They must have programming experience in languages such as SQL, Python, R, Scala, Java or SAS, along with a deep understanding of Extract, Transform and Load (ETL) methodologies and practices. Agile development and DevOps experience are also critical Data Engineering skill sets, especially when it comes to implementing data infrastructure. In addition to being coding experts, Data Engineers need to be highly focused on keeping things running for the organization. Their actions must keep downtime to a minimum, meaning the data infrastructure they build needs to be flexible, robust and reliable enough to withstand change without impacting uptime, which is critically important.
Technology Agnostic
Data Engineers must be fluent in using an array of technologies ranging from Apache Spark and Hadoop to Amazon Athena and Redshift. They must also be aware of the advantages and disadvantages of using one technology over another and why certain technologies are more applicable in heavily regulated industries. A good Data Engineer will be technology agnostic and able to quickly identify which technology is most suitable for the project at hand. There are upwards of 30 technologies widely used today, but this list is ever-changing. So Data Engineers must be adaptable and curious, with a lifelong love of learning – something ProCogia always looks for in new additions to the practice of Data Engineering. In terms of building scalable data pipelines, in-depth knowledge of the major cloud providers is another key requirement. Data Engineers must be experienced across Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services, at the very least.
Data Engineering Experience
We cannot over-emphasize the importance of real-world experience in the field of Data Engineering. Data Engineers are likely to have a relevant degree in Computer Science, Engineering, Applied Mathematics or Physics, but the valuable knowledge and insight gained while solving complex Data Engineering challenges for real clients is unmatched.
Client-focused
Engaging with clients, effectively managing multiple stakeholder requirements and having the ability to deliver effective results on time and within a budget is vital for any successful Data Engineer. A Data Engineer will typically work with numerous internal teams within any given organization. These organizations are typically looking to grow and improve their business by optimizing their data and workflows. However it’s rare all stakeholders are in alignment and it’s the job of an effective Data Engineer to ensure stakeholders buy into a solution that exhibits the whole organization’s strategic goals. Good communication skills are a must-have for Data Engineers. At ProCogia, they do not work in isolation – they must interact well with the wider data team and all the ‘data stakeholders’ across the organization. If a Data Science team has a specific tool or model they want to build, for instance, the Data Engineer is tasked with setting it up in such a way that they can use it efficiently to improve their workflow.
ProCogia’s Data Engineering Practice
A growing number of organizations are looking to bring Data Engineers into their teams and onto their payrolls. However, this is creating a skills shortage and, as a result, recruiting good Data Engineers with all the qualities listed above is becoming a costly exercise. But due to the rapidly increasing importance of the Data Engineering role – driven by companies generating large amounts of data and leveraging the cloud – there is no room for compromise. Put simply, you need an experienced Data Engineer to effectively manage your data systems. That’s why many enterprises are reaching out to ProCogia’s team of dedicated Data Engineers, rather than employing someone full-time. With a wealth of experience gained working for many clients across multiple industries, our Data Engineering team can support your organization to provide the right level of cost-effective data services at every stage of your data journey. Read our case studies to find out more.
Find out more about our Data Engineering practice
In a world where big data is driving huge value for organizations across many industries, Data Engineers are in demand. The recruitment rate of Data Engineers shows a 33% annual increase according to last year’s Emerging Jobs Report. This blog explains what Data Engineers do, what their vital qualities are, and why outsourcing Data Engineering projects is the way forward for many enterprises.
The Data Engineer’s Role
A Data Engineer effectively completes all the groundwork that enables BI, Analytics and Data Science to work and provide value. Key responsibilities include:
- Data ETL – moving data to a unified repository – typically a data warehouse
- Data pipelines – to clean and prepare raw data, apply transformations, and provide infrastructure to draw meaningful insights
- Data collection – obtaining and integrating data sets for an organization
- Data algorithms – to facilitate data science (BI, data analytics, machine learning and so forth)
- Data architecture design – to create a data-driven organization
4 Essential Data Engineer Qualities
Development Skills
Strong development skills are a must for successful Data Engineers. They must have programming experience in languages such as SQL, Python, R, Scala, Java or SAS, along with a deep understanding of Extract, Transform and Load (ETL) methodologies and practices. Agile development and DevOps experience are also critical Data Engineering skill sets, especially when it comes to implementing data infrastructure. In addition to being coding experts, Data Engineers need to be highly focused on keeping things running for the organization. Their actions must keep downtime to a minimum, meaning the data infrastructure they build needs to be flexible, robust and reliable enough to withstand change without impacting uptime, which is critically important.
Technology Agnostic
Data Engineers must be fluent in using an array of technologies ranging from Apache Spark and Hadoop to Amazon Athena and Redshift. They must also be aware of the advantages and disadvantages of using one technology over another and why certain technologies are more applicable in heavily regulated industries. A good Data Engineer will be technology agnostic and able to quickly identify which technology is most suitable for the project at hand. There are upwards of 30 technologies widely used today, but this list is ever-changing. So Data Engineers must be adaptable and curious, with a lifelong love of learning – something ProCogia always looks for in new additions to the practice of Data Engineering. In terms of building scalable data pipelines, in-depth knowledge of the major cloud providers is another key requirement. Data Engineers must be experienced across Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services, at the very least.
Data Engineering Experience
We cannot over-emphasize the importance of real-world experience in the field of Data Engineering. Data Engineers are likely to have a relevant degree in Computer Science, Engineering, Applied Mathematics or Physics, but the valuable knowledge and insight gained while solving complex Data Engineering challenges for real clients is unmatched.
Client-focused
Engaging with clients, effectively managing multiple stakeholder requirements and having the ability to deliver effective results on time and within a budget is vital for any successful Data Engineer. A Data Engineer will typically work with numerous internal teams within any given organization. These organizations are typically looking to grow and improve their business by optimizing their data and workflows. However it’s rare all stakeholders are in alignment and it’s the job of an effective Data Engineer to ensure stakeholders buy into a solution that exhibits the whole organization’s strategic goals. Good communication skills are a must-have for Data Engineers. At ProCogia, they do not work in isolation – they must interact well with the wider data team and all the ‘data stakeholders’ across the organization. If a Data Science team has a specific tool or model they want to build, for instance, the Data Engineer is tasked with setting it up in such a way that they can use it efficiently to improve their workflow.
ProCogia’s Data Engineering Practice
A growing number of organizations are looking to bring Data Engineers into their teams and onto their payrolls. However, this is creating a skills shortage and, as a result, recruiting good Data Engineers with all the qualities listed above is becoming a costly exercise. But due to the rapidly increasing importance of the Data Engineering role – driven by companies generating large amounts of data and leveraging the cloud – there is no room for compromise. Put simply, you need an experienced Data Engineer to effectively manage your data systems. That’s why many enterprises are reaching out to ProCogia’s team of dedicated Data Engineers, rather than employing someone full-time. With a wealth of experience gained working for many clients across multiple industries, our Data Engineering team can support your organization to provide the right level of cost-effective data services at every stage of your data journey. Read our case studies to find out more.
