Advancing Your Career Within Analytics

In my last post, I described what analytics means to me. I started with the short answer: “using data to drive better business outcomes.” Then I expanded on that idea by outlining eight key skills that fall under the umbrella of analytics. These skills, ordered loosely in terms of their position in the data lifecycle, are KPI definition, data collection, data management, self-service, reporting, data analysis, experimentation, and model-building.

When I looked back over my list, I noticed that some skills naturally group into distinct career paths, representing common ways that analytics professionals grow their expertise. These pathways can offer guidance for fledgling analysts deciding on their next steps. Leaders can also use them to identify skill gaps and develop their teams.

My idea got an extra boost during a lobby bar discussion I had at Superweek, the fantastic analytics conference I’m currently attending. A fellow participant asked me for some advice. Through her work as an analytics consultant, she had developed a fair amount of expertise building dashboards and reports, but wanted to expand into adjacent areas. She asked me: What paths did I see as possible options for her to pursue? As we brainstormed, I suggested she could deepen her focus on data analysis (especially insight generation) or explore a shift into data engineering by building tools and platforms. Her question reminded me how important it is for data folks to understand their career options.

In case my thinking can help others, let’s explore skill pathways within analytics.

1. Core Analyst

(KPI definition, reporting, data analysis)

All data analysts are certain to encounter three key responsibilities: defining KPIs, creating reports, and analyzing data. In many organizations, this is referred to as business intelligence. To thrive as an analyst, you must develop a strong command of the skills that will enable you to deliver high-quality work aimed at influencing business decisions.

As you build experience, you may choose to specialize. Some of the most impactful and highly valued analysts I’ve worked with are the ones who get really good at delivering insights and recommendations (the heart of “analysis”), especially to executive audiences. Or, if you’ve got a talent for blending quant skills with graphic design, data visualization can be a great way to stand out.

2. Data Engineering Pathway

(Data collection, data management, self-service)

I recognize that there are a variety of entry points into the field of analytics, but in this article, I’ve assumed that core data analyst work is a common starting point for skill development. No matter where you start out, I believe there is a natural connection between business intelligence and data engineering. 

Data engineers focus on designing, building, and maintaining analytics infrastructure ("the pipes through which the data flows"). They are often also responsible for transforming raw data into structured datasets that can be used by both people and systems. In some companies, data engineers also manage the software that supports self-service analytics, such as dashboarding tools or platforms for tracking user behavior.

For analysts considering a move into data engineering roles, it’s valuable to develop deeper expertise with data collection and data management. These skills sit at the intersection of what data analysts and data engineers do, and they are typically in high demand (so if you want to contribute, jump right in!). If you enjoy optimizing infrastructure and standing up self-service platforms more than business-facing insight work, this might be a good path for you.

3. Data Science Pathway

(Experimentation, model-building)

Data science has been a compelling next step for many data analysts over the years. The “sexiness” factor may have tempered a bit, but genuine curiosity about statistical methods and their impact on business outcomes is something that will never go out of style. Analysts who take this pathway often specialize in experimentation or model-building. This type of work can mean narrowing your focus, but it can also be highly rewarding for those who enjoy tackling complex problems with rigor and depth.

Opportunities in this space can vary quite a bit depending on your company’s culture. In some organizations, structured data science programs already exist, and analysts with a strong connection to the business are well-positioned to contribute to more science-focused work. In less mature environments, these opportunities may not be as plentiful. If experimentation is not yet a core part of decision-making, you may have a chance to pioneer it, either by running a proof of concept to demonstrate initial value, or advocating for more systematic testing. Either way, an analyst's understanding of business data and context (plus statistical fluency) will be an asset when transitioning into data science.

4. Data Leader Pathway

(The full bingo card)

If you want to move into an analytics leadership role, it’s important for you to have a working knowledge of all eight competencies. You don’t need to be an expert in every area, but leaders who understand the strategic potential of each discipline will be in a much better position to value their team’s contributions. This knowledge helps leaders set informed priorities and design thorough plans. Realistically, your individual skills will remain T-shaped, with deep expertise in one or two areas and broad familiarity across others. That said, having a holistic view of the full analytics landscape will help you succeed as a leader.

Some of the best leaders I’ve worked with and for are the ones who have a balanced appreciation for all aspects of analytics. It can be too easy to concentrate on flashy deliverables that impress executives, but strong leaders also recognize and highlight foundational accomplishments (especially if they are not as naturally visible) like infrastructure migrations or critical dashboards.

Choosing Your Path

Wherever you go next, stay curious, keep learning, and seek out the work that excites you most. Think about the unique value you bring (or want to bring) to your organization as you build areas of expertise. I hope this helps you explore new possibilities!

See Also

This article focuses on specializing and advancing within analytics. For a broader perspective on career moves across and beyond analytics, check out another piece I wrote last year.

P.S. I’m also publishing on Substack: measurecraft.substack.com