What an Analytics Engineer does on a managed data engineering team.

Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.

Analytics Engineer skills and technology we assign for

  • Building and maintaining dbt models, tests and documentation.

  • Defining metrics once, so two dashboards stop disagreeing about revenue.

  • Owning the semantic layer that reporting tools read from.

  • Retiring unused models, because an unmaintained dbt project becomes as untrustworthy as no models at all.

  • Working with analysts to turn recurring ad hoc queries into maintained models.

Seniority levels we assign.

Mid to senior. The value is in modelling judgement rather than dbt syntax. Seniority is one of the inputs in how the monthly fee is built.

When an Analytics Engineer is the right assignment.

Assigned where the data exists but nobody trusts the numbers coming out of it. On an ongoing roadmap this role is most often assigned alongside a Data Engineer or an ETL Developer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. At Azendo this sits inside data engineering outsourcing, on the same agreement as the rest of the team.

160 h
Typical monthly capacity for this role
Fixed
Monthly price, unaffected by leave or holidays
4–6 weeks
From signed scope to delivery starting
Monthly
Cycle to raise or lower committed hours

Questions about assigning an Analytics Engineer.

Do we need an analytics engineer or a data engineer?

Data engineer if the pipelines are broken. Analytics engineer if the data arrives but nobody trusts the numbers coming out of it.

Will you document the models?

Yes, in dbt alongside the code. Documentation that lives elsewhere stops being true within a quarter.

Assign a Analytics Engineer to your roadmap.

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