Overview
Building a data team means hiring engineers and analysts who can collect, process, and derive insights from your company's data. Unlike general engineering teams, data teams require specialized skills in data pipelines, analytics, and often machine learning.
A well-built data team typically includes:
- Data Engineers — Build and maintain data infrastructure, pipelines, and ETL processes
- Data Scientists — Build models, run experiments, and derive predictive insights
- Data Analysts — Create reports, dashboards, and answer business questions
- Analytics Engineers — Bridge between engineering and analytics, maintain data models
The composition depends on your needs: early-stage companies often start with one data engineer, then add analysts as data volume grows. Companies focused on ML products need data scientists earlier.
Team Composition Strategy
The Foundation: Your First Data Hire
Data Engineer (First Hire)
- Sets up data infrastructure (warehouses, pipelines, ETL)
- Builds data collection systems
- Ensures data quality and reliability
- Creates foundation for analytics
- Critical first hire—everything else depends on this
Why Data Engineer First:
- Without infrastructure, analysts and scientists can't work
- Data quality problems compound over time
- Early architectural decisions affect everything later
- One strong data engineer can support 2-3 analysts
Scaling to 3-5 Person Team
Option A: Analytics-Focused (Most Common)
- Data Engineer (infrastructure)
- Data Analyst (business insights)
- Analytics Engineer (data modeling)
- Additional Analyst (as volume grows)
Option B: ML-Focused
- Data Engineer (infrastructure)
- Data Scientist (models and experiments)
- ML Engineer (production ML systems)
- Data Analyst (business metrics)
Option C: Balanced
- Data Engineer (infrastructure)
- Data Scientist (models and insights)
- Data Analyst (business reporting)
- Analytics Engineer (data quality and modeling)
When to Add Specialists
Add Data Scientists when:
- You need predictive models or ML products
- You have enough data to train models
- Business questions require advanced analytics
Add Analytics Engineers when:
- Data models become complex
- You need to bridge engineering and analytics
- Data quality becomes a bottleneck
Add ML Engineers when:
- You're deploying ML models to production
- You need real-time inference systems
- Model performance and monitoring are critical
Hiring Order Matters
Phase 1: Data Engineer (Weeks 1-10)
Why First:
- Everything depends on data infrastructure
- Sets up pipelines and data warehouse
- Establishes data quality standards
- Creates foundation for analytics
What to Look For:
- 3-5+ years data engineering experience
- Experience with modern data stack (Snowflake, BigQuery, dbt, Airflow)
- Strong SQL and Python skills
- Understanding of data modeling
- Can work independently
Phase 2: Data Analyst or Scientist (Weeks 6-12)
Choose Analyst if:
- You need business reporting and dashboards
- Questions are mostly descriptive ("what happened?")
- You're earlier stage with less data
Choose Scientist if:
- You need predictive models
- Questions are predictive ("what will happen?")
- You have enough data for ML
What to Look For:
- 2-4 years experience
- Strong SQL and Python/R skills
- Business acumen (for analysts)
- Statistical knowledge (for scientists)
- Communication skills
Phase 3: Analytics Engineer or Additional Analyst (Months 3-6)
Add Analytics Engineer when:
- Data models become complex
- You need better data quality processes
- Analysts are spending too much time on data prep
Add Additional Analyst when:
- Volume of questions increases
- You need more specialized domain expertise
- Team is bottlenecked on analytics
Skills to Look For
Data Engineer Skills
Must-Have:
- SQL expertise (complex queries, optimization)
- Python or Scala for data processing
- Experience with data warehouses (Snowflake, BigQuery, Redshift)
- ETL/ELT pipeline tools (Airflow, dbt, Fivetran)
- Data modeling (star schema, dimensional modeling)
Nice-to-Have:
- Cloud platforms (AWS, GCP, Azure)
- Streaming data (Kafka, Kinesis)
- Data quality tools (Great Expectations, dbt tests)
- Infrastructure as code (Terraform)
Data Analyst Skills
Must-Have:
- SQL expertise
- Python or R for analysis
- Data visualization (Tableau, Looker, Metabase)
- Business acumen
- Communication skills
Nice-to-Have:
- Statistical analysis
- A/B testing
- Domain expertise in your industry
Data Scientist Skills
Must-Have:
- Python or R
- Statistical modeling
- Machine learning fundamentals
- SQL
- Experimentation (A/B tests)
Nice-to-Have:
- Deep learning
- MLOps
- Domain expertise
- Production ML experience
Budget Planning
Salary Costs (US, 2026)
| Role | Salary Range | Total with Benefits |
|---|---|---|
| Senior Data Engineer | $160-220K | $195-270K |
| Data Engineer | $130-170K | $160-210K |
| Data Scientist | $140-190K | $170-235K |
| Analytics Engineer | $130-170K | $160-210K |
| Data Analyst | $90-130K | $110-160K |
3-Person Team: $480K-650K annually
5-Person Team: $700K-950K annually
Other Costs
- Data Infrastructure: $5-20K/month (warehouses, tools, storage)
- Analytics Tools: $2-5K/month (BI tools, data quality tools)
- Recruiting: 20-25% of salary if using agencies
- Equipment: $3-5K per person
- Training: $2-5K per person annually
Common Mistakes
1. Hiring Data Scientists Before Data Engineers
Problem: Scientists can't work without clean, accessible data. They end up doing data engineering work.
Better approach: Hire data engineer first to build infrastructure, then add scientists when you have data ready.
2. Not Defining Clear Roles
Problem: Unclear boundaries between engineers, analysts, and scientists leads to confusion and inefficiency.
Better approach: Define responsibilities clearly: engineers build infrastructure, analysts answer business questions, scientists build models.
3. Ignoring Data Quality
Problem: Building on bad data creates wrong insights and erodes trust.
Better approach: Invest in data quality from the start. Data engineer should establish quality checks and monitoring.
4. Over-Engineering Early
Problem: Building complex data infrastructure when simple solutions would work.
Better approach: Start simple (managed services, basic pipelines), add complexity as needs grow.
5. Hiring Without Business Context
Problem: Data team works in isolation, building things business doesn't need.
Better approach: Ensure data team understands business goals. Analysts especially need business acumen.
Data Team Culture
What Great Data Teams Have
1. Data Quality Focus
- Automated quality checks
- Clear data definitions
- Documentation standards
- Monitoring and alerting
2. Business Alignment
- Regular syncs with business stakeholders
- Clear understanding of business goals
- Proactive insights, not just reactive reporting
3. Self-Service Analytics
- Tools and training for non-data people
- Clear documentation
- Reusable data models
4. Experimentation Culture
- A/B testing infrastructure
- Statistical rigor
- Learning from experiments
How to Establish Culture
Start with Infrastructure: Data engineer sets the foundation and quality standards.
Document Everything: Data definitions, pipeline logic, business metrics.
Regular Communication: Weekly syncs with stakeholders, monthly team reviews.
Celebrate Wins: Share insights that drive business decisions.
Interview Strategy
What to Assess
Technical Skills:
- SQL (for all roles)
- Python/R (for engineers and scientists)
- Data modeling (for engineers and analytics engineers)
- Statistical knowledge (for scientists)
- Business acumen (for analysts)
Problem-Solving:
- Can they break down complex data problems?
- Do they ask clarifying questions?
- Can they explain technical concepts simply?
Communication:
- Can they translate data to business insights?
- Do they tell stories with data?
- Can they work with non-technical stakeholders?
Red Flags
- Can't write complex SQL
- Doesn't understand business context
- Over-engineers simple problems
- Can't explain their work to non-technical people
- No attention to data quality
Timeline Expectations
Realistic Hiring Timeline
| Phase | Duration | Notes |
|---|---|---|
| Find Data Engineer | 6-10 weeks | Don't rush—critical hire |
| First Analyst/Scientist | 4-6 weeks | Can start after engineer hired |
| Additional Team Members | 4-6 weeks each | Can hire in parallel |
Total: 3-5 months to build a 3-person team
Factors Affecting Timeline
- Data engineering talent is scarce — Plan for longer timelines
- Remote expands pool — Consider remote-first
- Company stage — Early-stage takes longer
- Compensation — Competitive offers attract faster
Recruiter's Cheat Sheet
Key Insights
- Data engineer is critical first hire — Don't compromise
- Define roles clearly — Engineers vs. analysts vs. scientists have different skills
- Business acumen matters — Especially for analysts
- Data quality is foundational — Invest early
- Start simple — Don't over-engineer infrastructure
Common Questions from Founders
"Do I need a data scientist or analyst?"
Analyst if you need reporting and dashboards. Scientist if you need predictive models or ML.
"When do I need a data engineer?"
As soon as you have meaningful data volume or multiple data sources. Don't wait until it's a crisis.
"How much does data infrastructure cost?"
$5-20K/month for warehouses and tools. Can start lower with managed services, scale as you grow.
"Can one person do everything?"
One strong data engineer can support 2-3 analysts, but you'll eventually need specialists as complexity grows.