Dedicated analytics engineers for your offshore team.
Offshore analytics engineers in Chiang Mai and Bangkok, working full time on your roadmap and managed by us. An analytics engineer turns raw warehouse tables into clean, documented models your analysts and leaders can trust.
One agreement and one fixed monthly fee, with a service delivery manager in Thailand who runs your team day to day.
This role is part of a dedicated data engineering team. It's staff augmentation, fully managed: take one role on its own, or the whole discipline as one delivery team.
One Analytics Engineer, assigned to your product and fully managed.
We assign an Analytics Engineer to your team full time and run everything around them: a service delivery manager who answers for the work, training and coaching, the office and equipment, and HR and payroll. The price below is for a mid-level Analytics Engineer; junior and senior levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One mid-level Analytics Engineer, full time, everything below included. All 27 things included ››
- Analytics engineer From USD 3,900 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for delivery
- A 1:1 with our Head of Delivery every two weeks
- Weekly delivery scoring and monthly capacity reports
- A Talent Success Manager for every specialist
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HR
We are the employer
- Recruitment and technical assessment
- Employment contracts
- Salary and payroll
- Tax and social security
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Facilitation
We provide the workplace
- A desk in our own office in Chiang Mai or Bangkok
- A Workplace Experience Manager on site
- Team events through the year
- IT support
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Equipment
We supply the tools
- Laptop and hardware
- Software licences
- LinkedIn Learning access
- Device security and management
What an Analytics Engineer does on a dedicated data engineering team.
They work between your data engineers and the people reading dashboards, in your dbt project, and agree every metric definition before they model it.
Analytics Engineer skills and technologies
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Building and maintaining dbt models, tests and documentation.
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Defining metrics once, so two dashboards stop disagreeing about revenue.
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Owning the semantic layer that reporting tools read from.
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Retiring unused models, because an unmaintained dbt project becomes as untrustworthy as no models at all.
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Working with analysts to turn recurring ad hoc queries into maintained models.
Analytics Engineer seniority levels.
Mid-level to senior. The value lies in modelling judgement as much as in dbt skills. Seniority is one of the inputs into how the monthly fee is built.
When your team needs an Analytics Engineer.
The right addition when the data exists but the numbers coming out of it are disputed. Often paired with a data engineer. Analytics engineers join as part of a dedicated data engineering team. It fits companies that hire remote developers for their data stack and need one trusted set of numbers.
What we look for in a dedicated Analytics Engineer.
We look for analytics engineers who are strict about definitions and fluent in dbt and SQL. They should push back when a metric is ambiguous and document a model for the analyst who inherits it.
A remote Analytics Engineer's first three months on your team.
- Weeks 1 to 2Reviewing your dbt project and the metrics people argue about most.
- Weeks 3 to 6Rebuilding the disputed models with tests and documentation.
- Months 2 to 3Owning the semantic layer and retiring the models your dashboards no longer read.
Questions about adding an Analytics Engineer to your team.
Do we need an analytics engineer or a data engineer?
A data engineer if the pipelines are unreliable or data arrives late. An analytics engineer if the data arrives and the numbers are disputed, because definitions differ between teams. Many companies need both eventually, and starting with the bigger problem gives the fastest return on the first hire.
Will you document the models?
Yes, in dbt alongside the code, so the documentation stays accurate as the models change. Every model gets a description, tests on key columns and a clear owner. Analysts and business users can then see where a number comes from and trust it without asking the data team every time.
Add remote analytics engineers to your team.
Tell us your roadmap and your stack, and we'll come back with the right profile, the capacity and a start date. You can also talk to a service delivery manager first.
Tell us what your analytics engineer would work on.
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We reply within one business day.
