What can we learn from the emergence of analytics engineers? bridging data gaps and building insights
The emergence of analytics engineering opens doors to exciting possibilities in the ever-evolving landscape of data careers. It’s a field suited for individuals with a diverse skill set and a knack for taking initiative. Exploring this career path not only unveils its potential value but also triggers personal reflection, guiding individuals to understand their strengths and aspirations more deeply.
What exactly is an analytics engineer? Coined by leaders in dbt (data build tool ), it is an evolution of the role data analyst doing more than whats expected to resolve common issues working on the field.
Understanding the term “analytics engineer” requires a closer look at the fusion of skills and responsibilities encapsulated within. Unlike traditional roles, this new breed of professionals marries analytical acumen with engineering precision. They not only analyze data but also construct robust pipelines and systems, akin to data engineers. This multifaceted approach is particularly vital in dynamic environments like startups, where the first data hire often shoulders the entire data workflow, laying the foundation for future scalability and insights.
⚠️ “The Gap”
On my experience, the gap between data infrastructure and analytics operations remains an evident challenge. Despite the abundance of structured data, navigating through it often leads to hurdles like establishing a source of truth, avoiding duplicated analyses, reconciling conflicting numbers, and managing an endless backlog of tasks.
🔩 “Engineering”
The word “engineering” makes the term “analytics engineer” more than just a title combining both data engineer and data analyst; it embodies a commitment to robust solutions and scalability. Central to analytics engineering is its adherence to engineering principles, ensuring solutions are functional, safe, reliable, and scalable. Comparing it to building a house, while a carpenter can construct a basic roof over one’s head, scaling that to a large 100-floor story building demands engineering finesse working with multiple teams and considering scale. Analytics engineers provide scalable and manageable solutions critical for evolving data needs.
👾 “As and early game hero and a mid-late game support/lead”
At the inception of data projects or innovation opportunities, businesses require individuals with diverse skill sets capable of handling various tasks in the unknown. The data practitioner, whether a data analyst/engineer/scientist who discovers the opportunity, may end up building a Proof of Concept (POC) to kickstart something. The skillset of analytics engineers may fill this void, offering comprehensive expertise that spans from initial data pipelines to sophisticated analysis — a spectrum where data analysts/scientists or engineers may specialise over time based on demand.
On well-established teams, I envision analytics engineers making significant contributions by elevating the working methodologies of analytics teams. They provide guidance and support on implementing best practices such as code collaboration (using version control), maintaining data integrity and quality assurance, as well as ensuring scalability and modularity of the code base. This transition isn’t without its challenges and demands, but integrating such skill sets into a team can greatly increase productivity, particularly in enhancing the capabilities of an analytics team.
🔨 “Tools”
Tools like dbt play a pivotal role in bridging this gap by providing a holistic solution encompassing testing, managed SQL runs, robust documentation, and ease of adoption for analytics professionals. This comprehensive approach resonates strongly with analytics backgrounds, providing efficient workflows and data-driven decision-making.
I have written a quick 3-min read as I was really amazed by this framework, heres the link:
https://medium.com/p/f1b88be4e22b
While businesses may question the necessity of this evolving role and face challenges in integrating such skill sets, the undeniable gap between data infrastructure and analytics demands attention. Analytics engineers offer a strategic advantage, ensuring self-serve analytics, addressing critical data gaps, and infusing established data teams with refined skills and leadership.
In my view, the analytics engineer role is poised for substantial growth across startups and established organizations due to its ability to fulfill demands for self-serve analytics, bridge infrastructure-analytic divides, and enrich data teams with enhanced skills and leadership capabilities.
References:
https://www.getdbt.com/what-is-analytics-engineering
https://www.xomnia.com/post/bridging-the-data-gap-unraveling-the-role-of-the-analytics-engineer/
