Crafting Your Data Team
Data Hiring Strategy for Startups
When it comes to building a data team for a startup, the process is exciting but can also be confusing. It lays the foundation for the organization's data strategy. One of the first decisions to make is choosing between hiring a leader who views the data team’s role as delivering dashboards & analyses vs a modern data stack innovator who knows that data can play a much bigger role in a company.
This choice will have a significant impact on the team's ability to deliver value over time. Today I’m going to explore what qualities I believe a startup should look for in a data team leader and common misconceptions to avoid.
What To Look For When Founding a Data Team
The success of a data program depends on a founding team member's ability to analyze the company's objectives, plans, and operations to determine the most appropriate data tooling and procurement, hiring plans, data collection methods, and data warehouse setup. Although data scientists and analysts play a crucial role in a company's data program, they may not possess the necessary skills to establish a fully functional data organization from system architecture to impact. Therefore, during the hiring process, candidates should be asked to present their vision for a full data platform and hiring strategy tailored to the company's specific needs. If they are unable to present a clear and concise plan for utilizing the latest tools and why they selected them, it is a red flag that they may not be a suitable fit for the position.
The ideal leader has a T-shaped skillset with broad experience working across many business units and deep experience in data analytics and infrastructure. Each department/function in a company has very different data needs, to highlight a few:
Marketing - Attribution, identity resolution, web analytics
Finance - ARR/LTV & monthly snapshotting
Sales - Pipeline generation, management, snapshotting
Operations - Help center, support desk, chat
Product - Activation, monetization, retention, behavioral event instrumentation
These needs all require different tooling for instrumentation, collection and analysis and the leader should ideally be familiar with each. Over indexing on the depth of analytics experience and ignoring cross-discipline experience, the leader will likely have blindspots related to one or several areas of the business where they will be unable to communicate or plan effectively with stakeholders at a startup’s pace. It is a bonus if the leader has seen a data stack implemented multiple times at different size companies as there are many pitfalls that can be avoided by having seen data needs evolve at various scales.
What qualities should they have?
There are a number of qualities that I have seen be successful when hiring data leaders (and data team members):
Optimism: This is by the far the most important one. With startup data, often everything is be broken, mis-instrumented, inaccurate, and unreliable. Their job is going to be to wake up every day and tackle cross functional/team data issues to provide internal users with high quality data. If they’re not optimistic, they will struggle to maintain the sense of urgency to raise the program to success.
Adaptability: Startup data and systems are going to change on a daily basis and the data infrastructure is going to need to change with it, rapidly. Hiring a highly structured data team leader may sound attractive but if they don’t architect for rapid change they will always be playing catch up. In addition to that, the data tooling landscape is growing exponentially with vast numbers of new companies making 0 to 1 technology changes that redefine the industry every year. Missing or ignoring these shifts will cost you extra headcount and cause you to fall behind competitors who can move rapidly.
Technical Aptitude: Data team members must absolutely have a strong technical background in data analytics, architecture and/or data science. They should have an understanding of data modeling methodologies, data warehousing, data system design and visualization platforms.
Business Acumen: Having a good understanding of the business and industry (or ability to pick it up quickly) will be critical to them driving impact. They should be able to identify the business problems and opportunities that can be addressed through data analytics. Hiring a data specialist with no experience in the business’s field will definitely slow down time to value from data.
Confidence and Candor: Building a robust and scalable data architecture requires standing up for solid data engineering principles and being able to defend high ROI system spend. If the data leader is unwilling to hold or defend unpopular opinions and instead agree with every business decision they will architect the business’s data strategy into a corner from which it may never escape (think layers upon layers of current state business logic only living in the data warehouse with no historical logs). In my experience, businesses will always push data teams to take on responsibilities that don’t fit their tooling or experience because they can do it “faster” than other teams. Those ad hoc projects come at a huge price when viewed as technical debt over time.
Owner Mindset: The data team will be building their own mini-startup inside your startup complete with system infrastructure, personnel, and outputting data products. They should be excited about the prospect of fully owning the process and being responsible for the outcomes even when they are difficult and cross functional.
Common Misconceptions
Let’s also look at a couple common misconceptions I’ve seen in startups just getting beginning their data journey.
Misconception #1: Hiring for Experience
Having worked in data and business intelligence for 15 years I believe I safely land in this category and I honestly see a lot of my experience as more of a liability than an asset these days. The rate at which ideas become obsolete has increased and new paradigms cause long held best practices to become obsolete. Some examples are cloud data warehousing, dbt, and generative AI. All of which should change the way data strategists build and plan for long term success.
There are of course some skills that have proved to be robust like SQL, having a data modeling methodology, and collaborating with stakeholders to frame great questions. At the current pace of change of tooling and techniques in the data industry today I spend an equal part of my time unlearning old habits as I do relying on my experience to solve new challenges.
The way that data practitioners strategize and execute depends on their current skillset and what they believe is possible. If I was still relying on techniques from even 5 years ago I would be costing the business a lot of lost time and extra headcount to accomplish what can now be done in a fraction of the time thanks to recent innovations in tooling & techniques (many of which are the purpose of this newsletter!)
Misconception #2: Hiring Data Scientists or Analysts First
Many startups prioritize hiring employees who can "create data products" rather than laying down a foundation for doing successful data work. This is because they want to quickly benefit from the work of those employees without realizing that scientists and analysts require several key technologies and data structures in place to do their work effectively. Typically, this involves starting their data journey by hiring an experienced data scientist and expecting them to quickly create machine learning models to deliver value. This approach causes a number of early setbacks and points of friction.
One data scientist who is equipped with high-functioning data architecture can produce better quality and quantity of data products than a whole team of data scientists who start with no infrastructure.
Data scientists are high-cost, high-ROI specialists who require reliable data architecture, advanced tools, and resources that many startups lack. Upon onboarding, they often have to spend several months educating the organization on the importance of building a data team while struggling to deliver projects with up-to-date value from static CSV files.
Data scientists and analysts often lack the skills, but more importantly the incentive to build scalable data warehouse infrastructure. Instead, they will be more likely to spend months building workable solutions that aren’t intended to scale or improve with time. This means each data project takes the same or longer than the one before it did. Having a strong data and analytics engineering pod in place will ensure that all the work that goes into processing data for data science projects is stored in the data warehouse and easily re-usable for other teams and projects in the future.
Data scientists are often not data engineers, data pipeline architects, or data team managers. This means that in the beginning they will need to heavily rely on IT and engineering resources from elsewhere in the business to get their basic data needs met. When they do extract data from systems and load it into data science tools they will likely not prioritize building a foundational ELT pipeline for the future. Their incentive is to build the fastest solution they can to one problem after another rather than hiring roles who can deliver strategic, scalable impact.
Hiring Plan
Each data team will need to be crafted to the organization they are a part of but I can offer a generic order of operations for hiring. If the business knows they will need at least 3 people on the data team in the next couple years I would highly recommend starting with data engineering. This is often overlooked since this data team member often does not turn out visible data products, yet they are the most important foundation for the team. Only after an engineer has been in place for a few weeks would I want to land the first lead analyst to begin building out the baseline reporting. All these roles are quite flexible though and need to be tailored to the exact scenario since many data practitioners have overlapping skills that blur into more than one of these categories. For example, an analytics engineer can certainly be hired earlier if they are also able to flex into doing generating reports and analyses which many can. This hiring plan lays the foundation for being able to deliver on ambitious data projects in months rather than years. They build out a high functioning data warehouse that can scale with the business rather than building ad hoc data flows for specific problems that take longer over time rather than getting faster and faster.
Have any thoughts or feedback? Find me on LinkedIn!