Choosing between a Data Engineer, Data Scientist, and ML Engineer can feel like three different bets on the same runway. Each role solves a distinct bottleneck, yet early-stage founders often group them together and risk an expensive mis-hire.

In this guide, you will see clear year-one cost benchmarks, a quick decision tree, and interview rubrics that map each role to a concrete business goal. Read on to learn how a Data Engineer steadies your pipelines, how a Data Scientist drives product insights, and how an ML Engineer turns models into real-time features so you can hire in the right order and keep cash burn predictable.

Year-One Cost Benchmarks

Before you fall in love with a job description, verify your budget. The median U.S. base salary for each role is listed below, based on Built In’s May 2025 data. Add about 20 percent for payroll taxes, benefits, and stock options to calculate the true Year-1 cash cost.

Role	Avg US Base Pay	Estimated Year-1 Cash*
Data Engineer	$125k	~$150k
Data Scientist	$127k	~$153k
ML Engineer	$158k	~$190k

*Cash = base + typical payroll costs, not equity.

The 5-Minute Decision Tree

Step	Ask Yourself →	If No → Hire	If Yes → Go to
1. Data Foundations	“Do we already have a trusted cloud warehouse with pipelines that run without babysitting?	Hire a Data Engineer	Step 2
2. Insights for Humans	“Are product or growth teams blocked by unanswered questions, cohort cuts, or A/B tests?”	Hire a Data Scientist	Step 3
3. ML in the Product	“Will customers interact with an ML-powered feature in the next 3–6 months?”	Hire an ML Engineer	You are done; revisit later

How to Use the Decision Tree

1. Start at Step 1.
2. Answer honestly. If your answer is No, that row shows the role you need first.
3. Stop there. Hire, onboard, and let that person unblock the next stage.
4. Re-run the tree every fund-raise or major roadmap shift.

Why This Order Works

• First, stable pipelines give every metric a single source of truth.
• Next, insights drive roadmap choices and fund-raise decks.
• Finally, production models add real-time magic only after data and analytics run smoothly.

Run the tree, hire with intent, and keep your runway intact.

Role Cheat Sheets

Data Engineer Responsibilities

A Data Engineer is your pipeline builder. They pull raw data from every app you use, clean it, and load it into a central warehouse like Snowflake or BigQuery. Using tools such as Airflow for scheduling and Terraform for infrastructure, they automate the entire flow and add testing frameworks (dbt, Great Expectations) so bad data never reaches the team.

Interview mini-rubric

Skill to Confirm	Ask About	Test
SQL mastery	Window functions, CTEs	Clean up a tangled query live
Cloud know-how	IAM roles, VPC basics	Sketch a secure ingestion path
Observability	Metrics vs. logs vs. traces	Walk through a failed job RCA

Data Scientist Responsibilities

A Data Scientist turns clean data into decisions. Working in notebooks, they run A/B tests, slice cohorts, and build forecasting or classification models. Their main output is insight in plain language, often supported by charts, that guides product and growth teams.

Interview mini-rubric

Skill to Confirm	Ask About	Test
Stats intuition	Power, p-values, Bayesian vs. frequentist	Spot errors in an A/B design
Storytelling	Turning data into action	Pitch a 5-slide insight deck
Tool fluency	Pandas / Polars / Plotly	Code an exploratory analysis

Machine Learning Engineer Responsibilities

A Machine Learning Engineer takes the Data Scientist’s model and makes it part of your product. They package the model in a service, deploy it behind an API, and monitor its performance in real time. They also handle CI/CD, cost tuning, and rollback plans to keep predictions fast, cheap, and reliable.

Interview mini-rubric

Skill to Confirm	Ask About	Test
Deployment depth	Canary, blue-green, shadow traffic	Diagram a zero-downtime rollout
MLOps mindset	Drift, decay, data contracts	Choose health metrics post-launch
Coding rigor	Tests and refactors	Tidy a spaghetti model script

Founder FAQ

Q: Can one unicorn handle it all?

Early on, yes. A senior data generalist can keep the lights on across engineering, analytics, and ML. However, market data compiled by Live Data Technologies and analyzed by Data Career Jumpstart shows that the average tenure for Data Engineers, Data Scientists, and related roles is only about 18 months. After that, most “unicorns” move on, forcing teams to rebuild knowledge from scratch.

Q: Contractor or full-time?

Need quick help clearing data stuck in different apps? Hire a contractor. Pay only for the hours you use. You can start fast, scale time up or down, and avoid payroll taxes or equity. Downsides: costs rise if the work grows, and contractors rarely build long-term product know-how.

If data work will be ongoing, bring the role in-house. You gain someone who owns the architecture and grows with the company.

Rule of thumb: Use a contractor for short, defined projects; hire full-time when data engineering is core to the roadmap.

Q: Where do VCs stand?

Andreessen Horowitz’s report stresses that investors now look for solid data plumbing before flashy models. The market is consolidating around warehouses, lakehouses, and reliable pipelines; only after that foundation is in place do they expect ML features. In short, clean pipelines first, models later.

Key Takeaways for Hiring Teams

1. Match the hire to the bottleneck. Fix data plumbing first with a Data Engineer, unlock product insights next with a Data Scientist, then tackle real-time features with an ML Engineer.
2. Use lean interview rubrics. Focus on three core signals per role to avoid drawn-out loops and keep offers consistent.
3. Re-run the decision tree often. Check it after every fundraise or major roadmap shift so hiring stays aligned with priorities.

Need a Hand? Hire with Kofi Group

If you’d rather skip the guesswork, Kofi Group can surface pre-vetted Data Engineers, Data Scientists, and ML Engineers who fit your stage, stack, and budget. We run the technical screens, and deliver a short list so you can make the right hire in weeks, not months.

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Reach out to Kofi Group and receive a curated shortlist of active and passive candidates within 14 days.

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