Data Engineering skills make transformation possible
This recognition of capturing and understanding data is the core for driving new possibilities. The growing availability of analytics, business intelligence, and data warehouses have enabled data access in forms that benefit companies to make better decisions. From invisible or trapped data in manual forms to digitised data that can be accessed, addressed, analysed, modelled, presented and used much more quickly.
These decisions come from having good data engineers to bring sense to the data that a company has within its realm. Whilst everyone revels around the pretty graphs and the analysis, these cannot materialise without a lot of data management capability. Data science cannot function well without good Data Engineering.
We see this a bit like an F1 race car. The driver gets the excitement of speeding along a track, and thrill of victory in front of a crowd. But the builder gets the joy of tuning engines, experimenting with different exhaust setups, and creating a powerful, robust, machine. Let’s explore the skills of the builder – the data engineer.
A data engineer transforms data into a useful format for analysis. They move and transform data into “pipelines” for the data scientists or analytical specialists.
Aside from a strong foundation in software engineering, data engineers need to be literate in programming languages used for statistical modeling and analysis, data warehousing solutions, and building data pipelines.
Key data engineering skills and tasks include:
Database systems management – Data engineers must know how to manipulate database management systems such as the standard SQL but also NoSQL databases which are non-tabular and come in a variety of types depending on their data model, such as a graph or document.
Data APIs – An interface used by software applications to access data. It allows two applications to communicate with each other for a specified task. Data engineers build APIs to enable data scientists and business intelligence analysts to query the data.
ETL routines – ETL (Extract, Transfer, Load) how data is extracted from a source and transformed into a format that can be analysed and loaded into a data warehouse. This uses processing techniques to help users analyse data relevant to a specific problem. The ETL routine pulls data from various sources, applies rules to the data according to business needs, and then loads the transformed data into a database or business intelligence capability so it can be used and viewed by people in the organisation.
Data warehousing – Data warehouses typically store large volumes of current and historical data for query and analysis. This data could come from many sources, such as CRM, Accounting, Line of Business and ERP software. The data is used for reporting, analytics, and data mining. Most employers expect entry-level engineers to be familiar with Amazon Web Services (AWS) and Microsoft Azure cloud services platforms with a whole ecosystem of data storage tools.
Machine learning – Machine learning algorithms or models help data scientists make predictions based on data. Data engineers need a knowledge of machine learning as it enables them to understand a data scientist’s needs better and get models into production which helps build more accurate pipelines.
Algorithms and data structures – Data engineers focus mostly on data filtering and data optimisation, however a knowledge of algorithms means they can understand the big picture as well as define checkpoints and end goals for the business problem.
Distributed systems – Such as Hadoop is an important data engineering capability. This allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines. Apache Spark is the most widely used programming tool and is written using the Scala programming language.
Programming languages – Java is widely used in data architecture frameworks. Scala is an extension of the Java language that is interoperable with Java as it runs on JVM. Python is the top programming language used for statistical analysis and modeling.
The above is just the main technical skills there are some key people and business skills that good data engineers need too.
Business & Communication – These are key for collaborating effectively. It’s important for a data engineer to show an understanding of the underlying business problem they are trying to solve and describe clearly how their work will help the bottom line.
Data engineers also interface with many people, other engineers, data analysts, developers, PM’s, CTO’s etc. They can also work with other teams or business units to gather requirements and define project scope.
Collaboration – Data engineers need to understand the expectations of the teams they’re working with, how frequently they need to be communicated with, and what their challenges are. Understanding where this work fits in helps a data engineer be of service to other teams and come up with better solutions.
Presentation abilities – Data engineers often present findings to stakeholders. Effective speaking and articulation of the work they do and what they have found and how to explain technical data concepts in the context of solving a business problem will make a data engineer a compelling team member and increase the chances that their recommendations will be acted upon.
Our DTL Data Engineers
We are very fortunate at DTL to have a number of very skilled and experienced Data and Software Engineers that do all of the above. Our clients projects would not have been possible without these key skills from our team. Having high quality data engineering capabilities makes a significant impact on how effectively organisations can move to being data-driven and change into the company that delivers exceptional customer service and is operationally continuously improving.
Let us know if you would like to find out more about our Data Engineering capabilities. We would be delighted to help your Digital Transformation.
