How to Build a Data Team: Roles, Hiring Order, and What to Pay in 2026

The quick answer

Most organisations build their data teams in the wrong order. They hire data scientists before they have a reliable data pipeline. They hire BI developers before they have a governed data model. They hire a VP of Data before they have the foundational engineering that gives that VP something to lead. The hiring sequence matters as much as the individual hires. Get the sequence wrong and each new hire spends most of their time on work that should have been done by a different role that was hired earlier — or the hire fails because the infrastructure they needed to be effective does not exist.

The five core data roles

Before building a hiring plan, be clear on what each role actually does. These titles are used inconsistently across the industry.

**Data Engineer** — builds and maintains the pipelines that move data from source systems to the data platform. Ingestion tools (Fivetran, ADF), pipeline orchestration (Airflow, Prefect), transformation (dbt), and the infrastructure they run on. The primary output is reliable, timely, tested data in the platform. Skills: Python, SQL, cloud platforms (Azure/AWS/GCP), dbt, pipeline tooling. Does not design the overall data architecture — implements it.

**Data Architect** — designs the overall data platform: the architecture pattern (lakehouse, warehouse, mesh), the technology stack, the data model, the governance framework, and the integration between layers. The primary output is architecture decisions, technical standards, and the blueprint that engineers implement. Skills: broad platform expertise (Snowflake/Databricks/Azure/AWS), data modelling, governance design, solution design. Works with engineers but does not primarily write pipeline code. See how to hire a data architect for the full breakdown.

**Analytics Engineer** — sits between data engineering and data analysis. Builds the governed data models that analysts query — the dbt models that define Silver and Gold layers, the semantic layer, the certified data sources. The primary output is clean, tested, documented data models. Skills: strong SQL, dbt, data modelling, some Python. The role did not widely exist before dbt emerged; it is now one of the most valuable roles in a modern data team.

**BI Developer / Data Analyst** — builds the dashboards, reports, and analyses that business stakeholders use. Primary tools: Tableau, Power BI, Looker. Translates business requirements into analytical outputs. The primary output is trusted, usable BI content that supports decisions. Skills: BI tooling (Tableau or Power BI), SQL, data model understanding, communication with business stakeholders.

**Data Scientist / ML Engineer** — builds statistical models, machine learning models, and AI systems. The primary output is predictive models, scoring systems, and AI applications. Skills: Python, ML libraries (scikit-learn, PyTorch, TensorFlow), MLflow, statistics. Requires a functioning data platform to be effective — data scientists without clean, reliable data spend most of their time on data preparation rather than modelling.

The right hiring sequence

Stage 1: Get the data in and reliable (Hire 1–2)

The first data hire should be a senior data engineer who can set up the data platform and build reliable ingestion pipelines from your highest-priority source systems. Without a functioning data platform, no downstream role is effective. Data scientists have no clean data. BI developers have no reliable source. Analysts spend their time reconciling data rather than analysing it.

The common mistake at Stage 1: hiring a data scientist or BI developer first because the business has a visible reporting need. This produces a BI developer who is also doing data engineering — because the pipeline they need does not exist — and doing both poorly.

At Stage 1, also decide on your data architecture. This is where engaging a data architect (externally, if you do not have one) to design the platform before engineers start building saves significant rework. A data platform built without an architecture design is rebuilt in 18 months.

Stage 2: Build the analytical layer (Hire 3–4)

With reliable data in the platform, the second hire is typically an analytics engineer (to build the governed data models) and a BI developer (to build the dashboards and reports that the business needs). These roles depend on each other — the analytics engineer provides the clean data models; the BI developer builds on them.

The second-stage mistake: skipping the analytics engineer and having the BI developer build both the data models and the dashboards. BI developers who build their own data models produce inconsistent, ungoverned models that break governance as the team scales.

Stage 3: Add analytical depth (Hire 5–7)

With a functioning data platform and a BI layer in place, the third stage adds analytical depth: more BI developers to scale dashboard coverage, a second data engineer to manage pipeline reliability and new source integrations, and — when the business has specific predictive use cases — a data scientist.

The data science hire at Stage 3 (not Stage 1) is the right sequence because the data they need is now clean, governed, and accessible. A data scientist hired at Stage 1 before the platform existed spends 80% of their time doing data preparation work that should be done by a data engineer.

Stage 4: Add leadership and specialisation (Hire 8+)

At 8+ data team members, a Head of Data or VP of Data becomes valuable — someone to manage the team, set the strategic direction, manage stakeholder relationships, and drive the data programme at an organisational level. Below 8 people, the cost of a senior leader is not justified by the team they have to lead.

Above 10 team members, specialisation starts to make sense: a dedicated platform engineer focused on infrastructure and cost, a data governance lead, a dedicated ML engineer. Below 10 people, these responsibilities are distributed across the existing team.

What to pay in 2026

Compensation for data roles varies significantly by geography (US salaries are materially higher than AU/UK), experience level, and organisation type (tech companies pay more than financial services; financial services pay more than retail and healthcare in most markets).

**Data Engineer** — US: $130,000–$200,000 base. Fully loaded (including superannuation, benefits, equity) add 20–30%. Senior (5+ years, cloud-native expertise, dbt proficient): top of range. Mid-level (2–4 years): $140,000–$165,000. Australia: AUD $120,000–$175,000 base.

**Analytics Engineer** — US: $120,000–$180,000 base. The role is in high demand and the talent pool is smaller than data engineering — expect to pay at the top of this range for candidates with strong dbt and data modelling experience.

**Data Architect** — US: $160,000–$280,000 base. The range is wide because the title covers everything from mid-level solution architects to senior principal architects at large enterprises. See how to hire a data architect for the detailed compensation breakdown and what differentiates candidates at different price points.

**BI Developer / Data Analyst** — US: $90,000–$160,000 base. Senior BI developers with Tableau or Power BI certification and strong data modelling skills: $130,000–$160,000. Mid-level analysts with strong SQL and BI tooling: $100,000–$130,000.

**Data Scientist** — US: $130,000–$210,000 base. ML Engineers with production deployment experience: top of range. Analytical data scientists focused on statistical modelling without production ML: mid range.

**Head of Data / VP of Data** — US: $200,000–$350,000 base plus equity. The range reflects team size, scope (analytics only vs full data platform), and industry. Strong candidates from tech companies command the top of this range.

When to use consulting or contracting instead of headcount

Headcount is the right model when the work is ongoing, requires deep institutional knowledge, and justifies the cost of a full-time salary plus benefits. Consulting or contracting is the right model when:

**You need capability faster than you can hire for it.** Senior data architects and principal engineers take 3–6 months to recruit. If you have a platform build starting in 6 weeks, engaging a consulting firm to design and lead the build while you recruit is faster than waiting for the hire.

**You need specific expertise for a defined-scope project.** A data architecture assessment, a Tableau Server to Cloud migration, a Snowflake performance optimisation engagement — these are bounded projects with defined outputs. Hiring a full-time employee for a 3-month project is not economically rational.

**You want to de-risk a key architectural decision.** Having an experienced external architect review your data platform design before you commit to a 2-year build costs far less than discovering the design was wrong 18 months in.

**You have a key-person dependency risk.** A Tableau environment or data platform managed by one person who is the only one who understands it is a risk. A managed service or fractional consulting arrangement distributes that knowledge.

The hybrid model that works well for mid-market organisations: a small internal data team (3–5 people) who own the business relationships, understand the business context, and provide continuity — supported by a consulting firm who provides the senior architectural depth and specialised capability that the internal team does not have full-time.

The most expensive mistakes in data team building

**Hiring data scientists before the platform is ready.** This produces data scientists who spend 80% of their time on data preparation and 20% on actual data science. It is expensive, demoralising for the hire, and produces outputs that are less reliable because the data foundation is not solid.

**Conflating data analyst and data engineer into one role.** "Data analyst who can also build pipelines" is a role that does both poorly. Analysts and engineers have different skills, different workflows, and different ways of thinking about problems. In small teams, some overlap is inevitable — but hiring for overlap as a primary strategy produces a team where nobody is excellent at anything.

**Hiring for current tools rather than foundational skills.** Tableau experience is easier to evaluate than data modelling skills, so organisations hire Tableau developers when they actually need analytics engineers. BI tooling changes; the ability to model data well, test transformations, and think about data architecture does not. Hire for foundational skills; the tools can be learned.

**Not investing in architecture before engineering.** Building a data platform without a data architect is like building a house without an architect. The first version works. The second iteration requires rework of everything built in the first. The third requires rebuilding from the foundations. Getting the architecture right — either by hiring a data architect or engaging one externally — before engineers start building produces a platform that scales without constant rework.

FAQs

Should our first data hire be a generalist or a specialist?

Your first data hire should be the role that unblocks the most downstream work. For most organisations, that is a senior data engineer who can build the data platform and reliable pipelines — because without that, no downstream analytical work is possible. A generalist "data analyst who can also do engineering" is a reasonable compromise for very small organisations, but this role scales poorly and tends to create technical debt that is expensive to unwind.

How big should a data team be for a company our size?

A rough benchmark: 1 data professional per 50–100 business staff for analytics-heavy organisations (financial services, healthcare, technology); 1 per 150–200 for organisations with lower analytical intensity. These benchmarks are very rough — the right size depends on the complexity of your data environment, the breadth of your analytics requirements, and how much of the work is handled by a managed service or consulting partner.

We have a data team but they're overwhelmed. What do we do?

An overwhelmed data team is usually one of two problems: the team is under-resourced for the scope of work, or the work is not well-prioritised and the team is working on everything simultaneously without focus. Before adding headcount, audit what the team is actually spending time on. In most cases, a significant portion of engineering hours goes to BAU pipeline maintenance and incident response that could be handled more efficiently with better pipeline architecture or a managed service. Freeing engineering time from maintenance work is often more impactful than adding headcount.

Our data architecture consulting practice works with organisations at every stage of data team building — from designing the platform that the first data engineer will build on, to providing the senior architectural depth that small internal teams do not have full-time. If you are building or scaling a data team and want an experienced view on the right structure for your situation, book a free 30-minute audit and we will tell you directly what we would do in your position.

A former Microsoft data architect audits your data foundation, identifies your top priorities, and sends you a written plan. Free. No pitch.