The Data Analyst: Have we created a dead-end job?
A Guest Post from Gu Xie, Head of Data Engineering at Group 1001
I’ve talked in the past about how I want to feature more voices in this newsletter. Recently, those have been my colleagues, like Austin Kronz talking about crafting the ultimate business case for data governance or Sharif Karmally’s interviews with data leaders about why data governance fails today.
As the co-founder of Atlan, I work with amazing data leaders from around the world, so, starting today, I’m excited to feature their hard-earned perspective and thoughtful insights right here.
Today’s issue is a guest post by Gu Xie, Head of Data Engineering at Group 1001.
The Data Analyst: Have we created a dead-end job?
Let me start with a bold statement: the data analyst role has been one of the most transformative additions to the modern workplace. But despite its importance, we’ve inadvertently created a career bottleneck — a dead-end job in the data world.
I’ve been thinking about this a lot lately. This role is incredibly important, yet it didn’t even exist a decade or two ago. Back then, work was split across three generic roles — business analysts, developers, and project managers. The rise of data created the demand for a specialized role that could not only sit at the intersection of business and technology but also bridge the gap between raw data and actionable insights.
Data analysts uniquely understand how business processes manifest in data, enabling organizations to unlock valuable insights that drive decision-making. While business analysts traditionally focused on scoping and requirements, data analysts brought something new — the ability to transform data into a strategic asset for decision-making.
While the data analyst role has grown in importance, the career path hasn’t kept pace. We ask analysts to do everything—wrangle data, build dashboards, analyze trends, and deliver insights—but where does this lead? What’s the next step?
For most analysts, the answer is either an unfulfilling grind in their current role or a leap into data science.
That’s not a career path. That’s a dead end.
Why analysts are burning out
You know the drill if you’ve ever worked as an analyst or managed one. Analysts are expected to juggle every tool in the tech stack, decipher vague business requirements, and churn out insights that magically fix every business problem.
But here’s the kicker: while analysts should be experts in the business, they’re often too caught up in arcane technical work and stuck in a task-based way of looking at things. Instead of focusing on business impact, they’re buried in tools, queries, and dashboards, leaving little time to understand the organization they serve.
I’ve seen too many analysts pushed to the brink of burnout because we’ve failed to define what success looks like for them. One day, they’re knee-deep in Structured Query Language (SQL), and the next, they’re exploring the technical minutiae of business strategies. Some analysts are building dashboards while others are building data models. And all too often, they’re doing this without a clear understanding of their value or a vision of what’s next.
What happens when there’s no clear growth path? Talented, capable people burn out. Talented, capable people leave.
The structural problem
The real issue isn’t just about analysts focusing on the wrong things — it’s about how the role is structured. Too often, the data analyst role is seen as junior-level work, with little recognition and thought about how it could evolve. In fact, most analysts I talk to are trying to change their title and do something else! In many organizations, analysts' progress levels can be poorly defined, arbitrary, or still fairly junior. You’re an analyst, and that’s it.
Because of this, when analysts think about their next steps, they gravitate toward specific technical skills that will allow them to change their title to data scientist, analytics engineer, or data engineer.
Why? Because those are the only tangible growth opportunities that are presented to them. The system doesn’t encourage them to deepen their understanding of the business or build leadership capabilities. This structural issue leaves analysts stuck, thinking they must change their role or leave the field entirely.
This is a massive waste of potential. Analysts sit at the intersection of business and data, a critical position in any organization, and their work already impacts so much of their business and its users. But they’re undervalued because of both the way their role is set up and a broader mindset across the data community that often sees analysts more as support than strategic partners.
This isn’t an issue specific to individual companies — it’s a systemic problem in how data leadership defines and nurtures the analyst role. We need to give data analysts a way to grow without abandoning the unique strengths they bring to the table.
The role of AI
The rise of AI is about to shake things up even more. And that’s not inherently bad — I believe AI is just as transformative as the advent of the personal computer and the internet a couple of decades ago.
Tools powered by generative AI are already automating many tasks analysts have traditionally owned. In some ways, this is exciting—it means less time spent on tedious work and more time for creativity and strategy. But it also raises a tricky question: if AI can do much of what analysts do today, what’s left for humans? And more importantly, where will the valuable, cleaned, “AI-ready” data come from?
One of the most thought-provoking talks I’ve heard recently came from Michelle Winter, a distinguished engineer at eBay. She described three future roles for data professionals in an AI-driven world:
Domain data experts: Specialists embedded in functions like marketing or finance with a deep understanding of business processes and how data can optimize processes.
Data product owners: Leaders who think of data as a product, focusing on its lifecycle, usability, and strategic value across the organization.
Data artisans: Highly skilled individuals who bring craftsmanship to data modeling and engineering tasks, focusing on quality and precision.
This exciting vision reframes the conversation around impact rather than tools. It’s no longer about who can write the best SQL query or build the fanciest dashboard, which might not matter in a few years. It’s about understanding the business and using data to drive meaningful change.
The way forward
So how do we fix this? I don’t have all the answers, but here’s where I’m starting.
Data analysts are not junior data scientists
One of the first steps is to recognize that data analysts and data scientists exist in fundamentally different domains, each with their own scope and purpose. Analysts excel at bridging the gap between data and business, delivering actionable insights that drive decisions. Data scientists, on the other hand, focus on advanced modeling, experimentation, and building predictive systems.
Too often, organizations treat analysts as junior data scientists, but this mindset is a disservice to both roles. Analysts shouldn't feel pressured to "upgrade" into data science just to find career growth. Instead, organizations should define the analyst role clearly, celebrating its unique strengths and creating pathways that allow analysts to deepen their expertise and increase their impact across progressively larger domains.
Shift the mindset to impact, not tools
Data leaders must take action to reimagine the data analyst role and demand more from it. Analysts spend a lot of time making reports and dashboards, but that isn’t their job. Their real value lies in helping organizations make better decisions. This could be unlocking the potential of a particular department using its data, discovering brand new business opportunities, or creating new lines of business entirely.
Leaders should set expectations beyond technical work and highlight how analysts’ insights can—and should—drive outcomes. This means more consistent communication and curiosity about their work's results, as well as creating metrics tied to impact (such as revenue growth, cost savings, or process improvements), not just deliverables.
Build a clear vision and career ladder
Finally, the data analyst role needs well-defined career progression. Analysts should be able to progress through levels like engineers (see the example career ladder below) with a clear vision for their future based primarily on the scope of their impact rather than story points or technical skills.
For example, a junior analyst could initially focus on acting as an individual contributor within a specific team. In contrast, more senior analysts would concentrate on driving more meaningful outcomes for more of the business. As the level of impact across departments grows, the title grows. Beyond that, there should be opportunities to specialize as a domain data expert, take ownership of data products, or transition into leadership and strategy roles.
A call to action
If you’re an analyst, I challenge you to stop defining yourself by your tools and start thinking about the business impact you want to have.
If you’re a leader, look hard at the ladder you’ve built. Are you creating opportunities for your team to climb, or are you leaving them stuck?
I don’t have all the answers, but I’m determined to figure this out. If this resonates with you, let’s start a conversation. We owe it to ourselves and the analysts to build something better.
The opinions expressed herein are those of Gu Xie and not necessarily those of his employer.
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