Building a Data Team from Scratch: Roles, Hiring Order, and Common Mistakes

Last updated: January 2026

Why Hiring Order Matters More Than You Think

Most companies get their first data hire wrong. They recruit a data scientist because it sounds impressive on the investor deck, then wonder why that $180K hire is writing SQL to reconcile mismatched CSV exports instead of building ML models. The problem isn't the person - it's the sequence.

A data team is a stack, and like any stack, layers depend on each other. You can't run models on data that hasn't been cleaned. You can't clean data that hasn't been ingested. And you can't ingest data without someone who understands both the source systems and the target warehouse. Get the order right, and each hire multiplies the previous one's impact. Get it wrong, and you've got expensive people doing work that's two levels below their skill set.

The Five Core Data Roles (What Each Actually Does)

Data Engineer

Builds and maintains the plumbing: ingestion pipelines, warehouse configuration, orchestration (Airflow, Dagster), data platform reliability. They're the ones who get paged when the nightly load fails at 3am. Core skills: SQL, Python, cloud infrastructure (AWS/Azure/GCP), and a deep understanding of how data moves between systems.

Analytics Engineer

The bridge between raw data and business-ready models. They work in dbt, write transformation logic, build the semantic layer, enforce naming conventions, and make sure the "revenue" column means the same thing everywhere. They're essentially software engineers who speak business.

Data Analyst

Answers business questions. Builds dashboards that actually get used, digs into anomalies, produces reports stakeholders rely on for decisions. Core skills: SQL, BI tools (Looker, Power BI, Metabase), statistics fundamentals, and the ability to translate data patterns into business language.

Data Scientist

Builds predictive models, runs experiments, does statistical analysis that goes beyond what a dashboard can answer. They need clean, well-modeled data to be effective - which is why hiring them before engineers and analysts is such a common trap. Core skills: Python/R, statistics, ML frameworks, and experiment design.

ML Engineer

Takes a data scientist's notebook prototype and turns it into a production system. Model serving, feature stores, monitoring for drift, retraining pipelines. This role only makes sense when you have production ML workloads - most companies don't need one until they have 3+ models in production.

Hiring Order by Company Stage

Seed Stage: The Data-Aware Backend Engineer

You don't need a data team yet. What you need is a backend engineer who understands event tracking, can set up basic analytics instrumentation (Segment, Rudderstack, or even raw event logging), and can query a database when the CEO asks "how many users signed up last week?" Budget: $0 additional headcount - this is a trait you hire for in your existing engineering team.

Series A: Your First Analytics Engineer

This person sets up dbt, connects your warehouse to a BI tool, builds the first 10-15 data models that answer your core business questions (revenue, churn, activation, funnel conversion). They should be comfortable with both SQL and stakeholder communication. One strong analytics engineer at this stage is worth more than a team of three specialists. Budget: 1 hire, $120-160K.

Series B: Data Engineer + Analyst

Your analytics engineer is now drowning. They're maintaining pipelines, building models, fixing data quality issues, AND answering ad-hoc questions from 5 different departments. Split the work: a data engineer takes over infrastructure and pipelines, a data analyst handles stakeholder requests and dashboards. Your analytics engineer can now focus on the transformation layer and data modeling. Budget: 2 hires, team of 3.

Series C+: Full Specialization

Now you can justify a data scientist (you finally have clean data for them to work with), an ML engineer (if you have production model requirements), and maybe a data platform engineer to manage the infrastructure. You might also need a data product manager - someone who prioritizes the backlog and makes sure the team builds what the business actually needs. Budget: team of 6-10.

The "Full-Stack Data Person" Myth

Yes, people who can do data engineering, analytics engineering, analysis, AND data science exist. They're called unicorns, and they cost $200-250K+. More importantly, even if you find one, they'll burn out within 18 months trying to do four jobs simultaneously. They also create a critical single point of failure - when they leave (and overworked unicorns always leave), your entire data function goes with them.

Instead of hunting unicorns, hire T-shaped people: deep expertise in one area, working knowledge across adjacent areas. An analytics engineer who can debug a pipeline issue or explain a dashboard to a VP is more valuable than someone who claims to do everything at a senior level.

When to Hire vs. Outsource

Outsource for bounded, well-defined projects: cloud migrations, initial warehouse setup, a specific pipeline build, or a digital transformation assessment. These have a clear start and end date, and external teams often have specialized migration experience your team doesn't.

Hire in-house for sustained operations: ongoing pipeline maintenance, iterative data modeling, ad-hoc analysis, and any work that requires deep domain knowledge of your business data. If someone needs to understand that "customer" means something different in the billing system vs. the CRM, that's institutional knowledge you want to keep internal.

The gray area: data quality and governance. You can outsource the framework design, but enforcement has to be internal. Nobody outside your company will care enough about your data quality at 2am on a Saturday.

Interview Approaches That Actually Work

Take-Home SQL Exercise > Whiteboard Algorithms

Give candidates a realistic dataset (messy, with nulls, duplicates, and a couple of schema quirks) and ask them to answer 3-4 business questions. You'll learn more from how they handle dirty data than from whether they can reverse a linked list on a whiteboard. Time-box it to 2-3 hours and pay candidates for their time.

Ask About Debugging Real Pipeline Failures

The question "Tell me about a time a pipeline broke in production and how you fixed it" reveals more than any technical quiz. You're looking for: did they have monitoring? How did they discover the issue? Did they fix the root cause or just the symptom? Did they add a test afterward?

Test for Communication Skills

Ask them to explain a technical concept to a non-technical audience. If a data engineer can't explain what a slowly changing dimension is to a product manager, they'll struggle in cross-functional environments. Data teams that can't communicate get sidelined.

Red Flags in Data Candidates

• Only knows tools, can't explain data modeling. Tools change every 2 years. Someone who can explain star schema vs. one big table design and articulate the tradeoffs will adapt to any tool. Someone who only knows "how to click things in Tableau" won't.
• Never worked with production systems. Academic data science projects with clean Kaggle datasets don't prepare people for the reality of production data. Ask about scale, failure modes, and on-call experience.
• Doesn't ask about data quality. If a candidate never asks "how clean is your data?" or "what's your testing strategy?" during the interview, they either haven't worked in production or they don't care about quality. Both are problems.
• Can't explain a past project end-to-end. "I built an ML model" is a yellow flag. "I identified the business problem, worked with stakeholders to define success metrics, sourced and cleaned the data, trained the model, deployed it, and tracked its business impact" is what you're looking for.

Organizational Structure: Centralized vs. Embedded

Centralized Data Team

Everyone reports to a Head of Data. Pros: consistent standards, shared tooling, knowledge transfer between members. Cons: prioritization conflicts (every department wants to be first in the queue), disconnection from business context.

Embedded (Fully Distributed)

Data people report to individual business units (marketing, product, finance). Pros: tight alignment with stakeholder needs, fast turnaround. Cons: duplicated work, inconsistent data definitions (marketing's "revenue" != finance's "revenue"), and isolation from data peers.

Hub and Spoke (The Sweet Spot)

A central data platform team owns infrastructure, standards, and shared models. Embedded analysts/engineers sit in business units but follow central standards and attend central team rituals. This works for teams of 5+. Below that, just stay centralized - you don't have enough people to split.

The Single Biggest Mistake: Hiring a Data Scientist First

We've seen this pattern at least a dozen times. A Series A company hires a data scientist as their first data person. Within 3 months, that data scientist is spending 80% of their time on data wrangling: writing Python scripts to pull data from APIs, cleaning spreadsheets, fighting with inconsistent date formats. The ML work they were hired for? Maybe 4 hours a week.

After 6-9 months, they're frustrated and leave. The company then hires a data engineer to clean things up, which is what they should have done from the start. Total cost of the mistake: $100-150K in salary, 6-9 months of lost time, and the opportunity cost of not having proper data infrastructure during a critical growth period.

A Gotcha About Job Postings

If your job posting says "5 years of Snowflake experience required," you're filtering out good candidates for no reason. Snowflake hasn't been mainstream long enough for most engineers to have 5 years on it. What you actually want is strong SQL proficiency, cloud warehouse experience (any platform counts), and demonstrated learning agility. The specific tool is learnable in weeks; the fundamentals take years.

Key Takeaways

• Hiring order matters: backend engineer (Seed) → analytics engineer (Series A) → data engineer + analyst (Series B) → full specialization (Series C+). Violating this order wastes money and talent.
• Don't hire a data scientist until you have clean, well-modeled data. Otherwise, you're paying $180K for someone to wrangle CSVs.
• Outsource bounded projects (migrations, warehouse setup). Hire in-house for sustained operations that require domain knowledge.
• Interview with take-home SQL on messy data, production debugging stories, and communication exercises - not whiteboard algorithms.
• Use hub-and-spoke org structure once your team exceeds 5 people. Stay centralized before that.

Frequently Asked Questions

What is the best first data hire for a startup?

At the seed stage, hire a data-aware backend engineer who can set up event tracking, build basic pipelines, and query databases. A dedicated data engineer or analyst is premature until you have enough data volume and business questions to justify the role, typically around Series A.

Should I hire a data scientist before a data engineer?

No. This is one of the most common and expensive mistakes. Data scientists need clean, reliable, well-modeled data to be effective. Without a data engineer to build pipelines and an analytics engineer to model the data, a data scientist will spend 80% of their time on data wrangling instead of actual analysis or ML.

When should I outsource data work instead of hiring?

Outsource for bounded, well-defined projects like cloud migrations, initial warehouse setup, or specific pipeline builds. Hire in-house for sustained operations, ongoing pipeline maintenance, and work that requires deep domain knowledge of your business data.

What is the difference between a data engineer and an analytics engineer?

Data engineers build and maintain the infrastructure: ingestion pipelines, warehouse configuration, orchestration, and data platform reliability. Analytics engineers work downstream, transforming raw data into clean business models using tools like dbt, building the semantic layer, and ensuring data quality for analysts and stakeholders.