Level Up Your RevOps Career

Master the Tech or Lead the Strategy?

Revenue Operations has rapidly become the beating heart of high-performing businesses.

It's the art and science of aligning sales, marketing, and customer success teams to drive efficient, predictable revenue growth. But within this dynamic field, a key question emerges for ambitious RevOps professionals: Where do you go next? How do you carve your path to advancement and maximize your impact?

This is where the exciting fork in the road appears.

Many RevOps professionals find themselves drawn to one of two distinct, yet interconnected, paths:

1. The Technical Path: Delve deep into the data that fuels RevOps. Become a master of the tools and technologies that drive insights, automation, and efficiency.

2. The Strategic Path: Ascend to the realm of revenue strategy, where you'll guide go-to-market initiatives, forecast growth, and shape the overall direction of revenue generation.

Think of it as developing "downward" into the technical foundations or "upward" into leadership and strategic oversight. Both directions offer incredible opportunities for career growth, impact, and personal satisfaction within the RevOps world.

Choose Wisely My Operator Friends

In the early stages of a RevOps career, the focus is often on building a foundational skillset – understanding CRM systems, data hygiene, process optimization, and the interplay between different revenue-generating functions. But as you gain experience, specializing in a particular direction can significantly accelerate your career trajectory.

What awaits you on each path?

The technical path offers the chance to become a true data wizard. You'll wield the power of SQL, Python, and potentially even machine learning to unlock deep insights from your company's revenue data. Imagine building predictive models that identify high-potential leads, automating tedious tasks to free up your team's time, or crafting compelling data visualizations that illuminate the path to revenue growth. This path is perfect for those who love problem-solving, enjoy working with technology, and thrive on extracting meaning from complex datasets.

The strategic path is for those with a vision for revenue growth. You'll rise through the ranks to lead RevOps teams, collaborate with executives on go-to-market strategies, and own the revenue forecasting process. This path demands strong leadership, communication, and analytical skills. You'll be responsible for making high-impact decisions that shape the future of the business, and you'll need the ability to influence and persuade stakeholders across the organization.

Of course, these paths are not mutually exclusive. In fact, the most effective RevOps professionals often possess a blend of both technical and strategic expertise. This "T-shaped" skillset allows them to bridge the gap between data and decision-making, leading to more informed strategies and better outcomes.

In this article, we'll explore both paths in detail, examining the key skills, benefits, and potential roles associated with each. We'll also discuss how to develop a T-shaped skillset and provide guidance on choosing the path that best aligns with your interests and career goals. Whether you aspire to be a data-driven RevOps specialist or a strategic leader guiding revenue growth, this article will equip you with the knowledge you need to level up your RevOps career.

The Technical Path: Become a RevOps Data Wizard

As systems get more complicated RevOps professionals with strong technical skills are in increasing demand. This path is all about diving deep into the data that drives revenue generation, using powerful tools and technologies to extract insights, automate processes, and optimize performance. If you have a knack for numbers, enjoy problem-solving, and thrive in a fast-paced environment, the technical path might be the perfect fit for you.  

Essential Technical Skills for RevOps Professionals

At its core, you need to be able to find data first. Then you need to learn how to move it. Then you learn how to manipulate and analyze it.

These are the core building blocks of data engineering and science.