Why is Data Engineering a Critical Element to Digital Transformation?

Enterprises today now have a board-level mandate to create a digital advantage by unifying data to glean business insights, faster and accurately. Diverse data sets and the volume continues to grow unabated, and as more organizations have realized over the past decade that data is critical for their business survival, the competition for key talent who can shepherd this transition has become fierce. The need for a deliberate digital transformation strategy is driven not only by the ever-increasing volumes of data that every business is challenged with managing, but also by the pressure to reform and streamline internal processes to match growing expectations of customers, both internal and external to the organization.

Data is at the core of a successful digital transformation, and many businesses have reached the tipping point where they will not be able to survive using legacy architecture, on-premises datacenters, and traditional operating procedures. All data and technology leaders, including those at companies that have never considered themselves digital-first, are being held to a higher standard to digitally transform their business to harness data that can deliver an exceptional user experience. 

Why Digital Transformation is Critical to Success

In order for an organization to be data-driven, they must be committed to pursuing constant improvements and modernizing their data architecture, tools, technologies, and process. For this type of transformational initiative, there is a heavy emphasis on both data and technology to improve everything from internal reporting, operations, and analytics to more outward-facing business practices. 

From balancing hybrid cloud deployments to enabling edge-based data processing for artificial intelligence and the Internet of Things, the sheer volume of implications for those implementing new technologies into business strategy is immense – but also offers a compelling competitive advantage. However, this means that legacy systems that have been in place for years (and sometimes decades) may find themselves being completely overhauled or abandoned, and there is a dire need for updated infrastructure and systems.

Regardless of the magnitude of the digital transformation or integration, data engineering is a critical key factor in facilitating these types of technology changes. Modernization requires not only a thorough understanding of the legacy systems, but also the new technology that will better serve the specific needs of the business. Organizations need architects and engineers with an immense depth of technical skill who can assist with migrating their existing systems, integrating those systems with newer ones, or even building new systems and process from the ground up.

What is the Role of Data Engineering in an Organization?

While “Big Data” may have emerged in the early 2000s, data professionals tasked with handling this massive influx of data (including roles like Database Administrators, SQL developers, and IT professionals) were not titled as Data Engineers quite yet. Now, data volumes, variety, and velocity are much greater than what they used to be, which has led data engineering professionals away from using traditional ETL tools to developing and adopting new tools and processes to handle the data revolution. This includes many of the tools we’ve been seeing on recent job descriptions, such as Databricks, Prefect, MLflow, Dagster, Beam, and Confluent, among many others.

Data engineering has evolved immensely since the early years with the industry-wide shift towards Big Data, digital transformation, and more sophisticated data science practices like machine learning and artificial intelligence. These modern tools and responsibilities now support hybrid cloud computing, data infrastructure, data warehousing, data mining, data modeling, data crunching, metadata management, data testing, and governance, among others. The right blend and implantation of these technologies can unlock data elasticity and reduce latency resulting in better business outcomes. As business needs continue to evolve, the technologies and tools used by organizations will follow suit, making data engineering professionals who can lead the next phase of a business’ digital transformation invaluable.

Why Data Engineering is the Crucial Piece to Digital Transformation

Because of IT’s significant investment (of both time and resources) in keeping existing systems functioning as opposed to driving innovation and change, the process of digital transformation will require exceptional leaders in Data Science, Data Analytics, and Data Engineering to push the organization forward. Digital transformation also requires better data quality and usually the integration of many disparate data sources, and these priorities are an ideal match for the skills and responsibilities of an effective data engineering team.

With the explosion in tools and technologies, many companies experience an integrations crisis as they simultaneously look to connect disparate tools, data sources, and leverage internal data for competitive advantage. Data engineers, as many know, often have a plethora of tools and skills at their disposal, and many of these roles come with a hefty list of technical requirements to be successful. While in the past some of this work has been left to data scientists, this is not the most effective use of their skillset, and tasks like building out data architecture and pipelines and tool integration is much better suited to the skillset of a data engineer.

Without skilled data engineers to create architecture and pipelines, and to integrate various systems and data sources, the skills of your data science team will go underutilized, and it will be exceedingly difficult, if not impossible, to achieve any data-forward strategy. We already know that becoming data-driven is not just advantageous – it is essential to company survival – and investing heavily into a data science team without hiring data engineers to support them is putting the cart before the horse.

Hiring Talent for Digital Transformation Efforts

As companies industry-wide continue to push towards cloud and edge technologies, we’re seeing more of the aforementioned Database Administrators and SQL developers expanding their education and skills to be more marketable. In some cases, it may make sense to bring some of this talent on board to train them on your own systems to push along digital transformation efforts along with your data engineering talent. If there isn’t the budget for the desired tech talent, it may also be beneficial to hire professionals who are more junior and who can be trained and developed through an “upskilling” program, but this requires a “speed to productivity” time investment. This approach has an added benefit of creating buy-in from professionals who feel that the organization has invested in their professional training but there is a risk of losing the newly skilled talent to another opportunity. We’ve seen many organizations start to leverage contractors as a successful way to onboard best-in-class talent and complete critical digital transformation project-based work. Often contract professionals bring with them a plethora of existing knowledge which is shared amongst the fulltime team and used across the organization. Also, contract professionals work well on project-based assignments, but sometimes offer the ability to convert fulltime once the project is complete. This allows for a “try before you buy” approach and eliminates the downside of losing newly developed institutional knowledge. 

Depending on a client’s budget, we’ve seen requests, for example, for data engineering candidates with both considerable Hadoop and cloud technology experience. In these cases, it may be easier to find someone who is well-versed in the legacy systems (i.e., Hadoop) and train them up on cloud technologies or hiring a consultant. However, these responsibilities can also be split out and have separate titles, with database administrators responsible for maintaining data pipelines, and then a separate team responsible for the cloud migration work. These ideas offer an easier hiring solution than searching for unicorn candidates that must have the skillset to cover every area at once in a fulltime capacity.

From what we’ve seen, hiring a strong data engineering team is an important element to a successful digital transformation. We’ve written at length about our strategies for identifying and hiring data engineering talent, and have also produced a salary report with additional information on salaries and other important information for hiring managers and data leaders. This summary has shared our perspective on how data engineering is a critical element to digital transformation initiatives, and in our full industry report you can find a plethora of data and insights that are crucial to managing talent strategy, including WFH, industry trends, salaries examined by various factors, attrition insights, and more. We’re able to provide this data to the community because of our unique position between both employers and data professionals, which gives us an exceptional vantage point on market trends. To access even more information to aid your battle in the talent war, you can download the full reports here.

Jon Linn, Executive Recruiter at Burtch Works, is one of our recruiting managers and has over seven years’ experience working with future 50 companies through start-ups on their digital transformation efforts. His expertise includes technical recruiting and professional consulting services. He works closely with Burtch Works’ clients, candidate network, and senior recruiters to develop data science talent acquisition strategies and expand upon our client relationships by addressing their unique business challenges.