Your First Data Hire: A Practical Guide

After founding Seekwell, and then working at Thoughtspot, and now building the best data platform for startups, I’ve seen way too many startups dealing with endless Excel sheets that all told different stories, making it impossible to trust their data. I’ve watched as these startups transitioned from data chaos to data analytics, and I’ve seen the good, the bad, and the ugly, and even a little bit of success.

I created this guide in part to help Definite’s customers navigate the transition but also to help startups everywhere learn from the generations that came before them. These are the lessons you shouldn’t have to learn the hard way when making your first data hire.

Bringing in a data pro doesn’t have to be daunting. With the right balance of technical know-how, communication skills, and an eye toward real business needs, you can transform all those confusing spreadsheets into clear insights that guide growth.

What to look for in your first data hire

Before diving into job titles or timelines, let’s clarify the three main skill sets you are probably thinking about. In my experience, companies often try to find all three in one person. That’s ambitious, but understanding your priorities will help you hire the best fit.

A. Data Infrastructure
Getting data out of tools and into a data platform or analysis-ready format is a real challenge. Data engineers typically handle this: building pipelines, setting up databases, and ensuring you can trust your data. It’s critical, but you can often handle this part-time with a consultant, especially in the early days.

B. Data Analysis and Business Intelligence
This is the ability to translate raw data into meaningful answers via SQL queries, tweaking charts, and building dashboards. Tools like Metabase, Tableau (or not), Google’s Looker Studio, and ThoughtSpot can make your life easier. Someone skilled here will help you figure out how many customers churned last month or why your conversion rates just tanked.

C. Business Acumen and Data Communication
Have you ever built a dashboard only to hear, “This doesn’t really answer the question I had”? A good data professional anticipates follow-up questions and sets up reports that go deeper. They’ll keep asking, “What does the business actually want to know?” and make sure they deliver value through tangible narrative—not just pretty charts.

! Word to the wise: Data science and machine learning can be exciting, but unless your data is already structured and your company has a clear use case, it’s often an unnecessary early focus. Prioritize B and C first. You can handle Data Infrastructure with a consultant if needed.

Now that we’ve established the fundamentals, let’s get into the details.

When is the right time to make the hire?

You might sense it’s time to hire when:

• Teams live in spreadsheet chaos, and you never know which numbers are accurate.
• You’ve got multiple systems collecting data, but no single source of truth.
• Leadership or investors keep asking questions nobody can answer confidently.
• Engineers are wasting time dealing with ad-hoc data tasks.

If your company already has some basic data collection tools (think Google Analytics, Mixpanel, HubSpot, or Stripe) and you’re ready to invest in a data platform, you’ve crossed the threshold to make a data hire worthwhile.

Who should your first data hire be?

It’s tempting to look for a “unicorn” who can handle everything from infrastructure to machine learning. In practice, that’s expensive—these are senior people and hard to find. Instead, zero in on your most pressing needs.

Role	When to Hire	Responsibilities	Pros	Cons
Data Analyst	Need immediate insights and quick wins	Write SQL queries, build dashboards, report actionable insights	Delivers quick value, more budget-friendly	May lack the engineering skills to fix complex data issues
Analytics Engineer	Need to transform raw data for better reporting	Use SQL and transformation tools to create business-friendly data sets	Bridges technical and analytical functions	Requires solid engineering knowledge
Data Engineer	Data is siloed and you need pipelines to scale	Build and maintain ETL processes and infrastructure	Ensures robust data flow	Expensive. Won’t necessarily produce business insights on their own
Data Scientist	You have a solid data foundation and a clear ML need	Predictive modeling, experimentation, advanced analytics	Can unlock strategic, long-term insights	Overkill if you lack clean, well-structured data

Most early-stage startups would benefit the most from a solid data analyst. If your data is an absolute mess, you might want an engineer or to bring in a consultant to set things up.

The hybrid approach: Consultant + Data Analyst

Why not just hire an all-in-one person? Besides the fact that you’ll probably spend more time looking for this unicorn than they will spend working at your company. This mythical expectation often leads to burnout, disappointment, or both. A more practical approach is the hybrid model.

A hybrid approach provides the technical expertise you need to get up and running smoothly with the long-term investment you need from a dedicated internal team member.

Consultant:
They’ll handle data infrastructure—the behind-the-scenes plumbing, data storage setup, and integration. You pay them for just a few months or hours a week, so you’re not stuck with a senior data engineer on full-time payroll when all you need is the initial architecture.

Technical Data Analyst:
They’ll own Data Analysis and Business Intelligence to build dashboards, analyze trends, and engage with business folks to determine what questions need answering. This role is usually full-time because the business will constantly need insights and new reports.

Role	Responsibilities	Time Commitment
Consultant (Data Engineer/Architect)	Set up and integrate the data platform, model data, and ensure data governance	Part-time (maybe three to six months)
Technical Data Analyst	Define key metrics, build dashboards, analyze trends, communicate insights	Full-time

This pairing will save you money, keep you flexible, and let each professional focus on what they do best.

Structuring the Hiring Process

Writing a job description that goes on and on about every possible skill can scare off great candidates. Focus on outcomes:

“We need someone to unify our customer data across multiple sources and deliver dashboards that help us spot product and marketing trends.”

Steps to Interview

1. Screening: Test SQL proficiency, data modeling, and how they think about business metrics.
2. Case Study: Give candidates a small sample of real (or realistic) data. Ask them to build a simple dashboard or run some analysis.
3. Technical Review: Dive into their reasoning. How do they handle unexpected data patterns? How do they communicate them?
4. Final Interviews: Bring in leadership and key stakeholders to ensure the candidate fits culturally and can communicate with non-technical teammates.

Pitfalls to avoid

Despite the best intentions, a few traps can sabotage your data journey:

• Mismanaging the Budget
  • Failing to set aside funds for a solid data stack.
  • Buying expensive tools too early, before you have someone who knows how to set them up.
• Hiring the Wrong Role
  • Expecting a data scientist to do data cleanup. (I don’t know how many times I’ve seen this fail.)
  • Bringing on a junior analyst when you need strategic leadership.
• Poor Integration
  • Not defining business metrics early.
  • Sequestering your data hire instead of embedding them with teams.

When I first started, I saw a company blow its entire budget on top-tier BI tools without having a single person who knew how to model data properly. Their visualizations were stunning but told contradictory stories — talk about confusion.

Scaling Beyond the First Hire

Once you have someone analyzing data and delivering insights, you might notice new problems cropping up.

Who to Hire Next?

• Data Engineer: If you find your data pipelines are unstable and your analyst is stuck waiting for new data to load, that’s your cue.
• Analytics Engineer: If your data is there but looks messy, you need someone skilled in transforming it into business-friendly tables.
• Data Scientist: When you have a solid data foundation and are ready for predictive modeling or deeper machine learning, it’s time to bring one on board.

Note: If you use Definite, you won’t need to worry about the first two options, and a data scientist can plug their work directly into Definite’s Python connector.

Building a Data Culture

• Encourage regular collaboration among analysts, engineers, and business teams.
• Standardize metrics so everyone speaks the same data language.
• Use version control and testing for anything that involves data transformation.

Finding good candidates

LinkedIn and your website's Careers section are the two obvious places to post your job. While LinkedIn is great for reach, you will be bombarded with low-quality and AI-generated applications, making it hard to sort through all of the applications.

I’ve seen customers have success posting data jobs on popular data-related slack groups on their #jobs channel. This is where a lot of really good candidates regularly hang out, but they are passively looking and won’t even bother applying for LinkedIn job posts.

For data analysts with a digital/web focus - Measure Slack is the #1 community with over 25k members. You can join the slack at https://join.measure.chat/

For a bit more of a technical and often more strategic focus around data engineering and analytics engineering, your best bet is the Locally Optimistic, with about 8k members: https://locallyoptimistic.com/

Please note: Both communities are very selective in terms of who can join (data practitioners only), but I’d be happy to post a data-related job for you - just contact me on LinkedIn

Summary

If you’re drowning in data but starving for insights, a dedicated data analyst who can communicate with non-technical folks will be your best friend. Meanwhile, bring in a consultant for a few months to tackle the infrastructure work and spare your full-time hire the madness of pipeline building.

Focus on the combination of skills that matter right now. Let the fancy machine learning projects wait until you have clean, trusted data. Hiring someone who truly understands the business questions and can anticipate the follow-up ones will pay off far more than a “unicorn” who tries to do everything.

Feeling inspired or just overwhelmed? I’d be happy to chat: mike@definite.app