A modern data team has six roles: data engineer, analytics engineer, data scientist, data analyst, data platform engineer, and data product manager. Few teams have all six; the right mix depends on org maturity and use cases.
Three organizational patterns dominate centralized, embedded, and hub-and-spoke each with strengths and trade-offs. This page covers what each role does and how to assemble the team.
Six Roles on a Modern Data Team
|
Role |
Primary job |
|
Data engineer |
Pipelines, ingestion, platform plumbing |
|
Analytics engineer |
dbt models, business-meaningful tables |
|
Data scientist |
ML models, statistical analysis |
|
Data analyst |
Business questions, dashboards, reports |
|
Data platform engineer |
Infrastructure, security, performance |
|
Data product manager |
Prioritization, requirements, stakeholder mgmt |
Data Engineer
Builds the ingestion, transformation, and serving layers. Owns reliability and pipeline architecture. Senior DEs design for scale and observability; junior DEs maintain and extend. The seven principles behind scalable data pipeline architecture define what "designed for scale" actually means in practice.
Analytics Engineer
Owns the dbt project. Translates raw data into business-meaningful tables that analysts and ML consume. Bridges DE and analyst roles.
Data Scientist
Builds predictive models, runs statistical analyses, designs experiments. Depends on data engineers and analytics engineers for clean inputs.
Data Analyst
Answers business questions, builds dashboards, surfaces insight. The role closest to the business; the one most affected by data quality upstream which is why data quality in analytics is a shared responsibility across the whole team, not just the analyst's problem.
Data Platform Engineer
Owns the infrastructure: cloud setup, security, IAM, cost, performance, on-call rotation for the platform. At mature organizations, this role operationalizes the data governance framework at the infrastructure level access controls, lineage tooling, quality enforcement. Distinct from DE work; some organizations combine the roles, others separate them.
Data Product Manager
Prioritizes the data backlog, gathers stakeholder requirements, owns roadmap. Increasingly important at scale; smaller teams blend this into engineering lead roles.
Team Shape Patterns
Centralized one team owns all data work, supports the business. Works at small scale, gets bottlenecked at large scale. Embedded data team members embed in business teams. Faster delivery; harder data consistency. Hub-and-spoke central platform team owns infrastructure and standards; embedded analytics engineers and analysts in business teams. Common pattern at scale.
Hiring Sequence by Org Size
Stage 0 (no data team): hire an analytics engineer who can also do basic DE. Stage 1 (first dedicated team): add a senior data engineer. Stage 2 (growing): add data analysts and a data scientist. Stage 3 (maturity): add data platform engineer and DPM. Hiring out of sequence DS before clean data, DPM before a team usually doesn’t work.
Centric helps design data team structures through its data engineering and warehousing service.
Frequently Asked Questions
What roles are on a data engineering team?
Six: data engineer, analytics engineer, data scientist, data analyst, data platform engineer, data product manager. Small teams blend roles; mature teams specialize.
What is the difference between a data engineer and an analytics engineer?
Data engineers build pipelines and platform plumbing. Analytics engineers build business-meaningful tables (often in dbt). Different jobs; both essential.
Should we hire a data scientist first?
Usually no without clean data and a working platform, the data scientist spends most of their time doing data engineering. Hire the engineer first and invest in scalable pipeline architecture before adding ML roles.
How big should the team be?
Highly variable. Early-stage might be one analytics engineer; large enterprise data teams have dozens. Match team size to use-case volume and org complexity, not to industry benchmarks.
Conclusion
Data engineering teams succeed when the structure matches the org's maturity and use cases. Start with the analytics engineer; add a senior DE; layer specialization as the program scales. Hub-and-spoke is the common end-state for mature organizations.
The team that ships is the team that's built deliberately. At Centric, we help organizations design data team structures that match their actual workloads and maturity not industry benchmarks.
