Analytics Engineer

JOB DESCRIPTION
Position :
ANALYTICS ENGINEER
Quantity : 02 ( 1 senior - 1 Middle)
Type : Full-time
Location : 5nd Floor, TTC Building, No.19 Duy Tan St., Dich Vong Hau Ward, Cau Giay Dist,. Hanoi Capital, Vietnam
Working location: HCM City/ Hanoi
ABOUT US
● Astek is a global player in engineering and technology consulting, founded in France in
Today, we are +7500 engineers across over 22 countries in the world.
● With expertise in many industrial and tertiary sectors, we support our international clients in the intelligent deployment of their products and services, and in the implementation of their digital transformation.
● Astek Vietnam is established in 2022 to increase group's capability to support our clients' operation around the globe, leveraging Vietnam talented engineers.
● More information about our group: [protected info]
JOB DESCRIPTION & REQUIREMENTS The short version
Every AI tool at - the agents that build our sales forecasts, the ones that answer questions about pipeline, the ones drafting our reporting - is only as good as the data underneath it. Somebody has to own that layer: keep the systems talking to each other, keep the numbers clean, keep the connections that AI tools query actually working.
That's this job. You'll learn how AI plumbing works by being the person who builds and maintains it.
Why this role exists
It is a B2B EdTech business selling to schools across North America, Australia/New Zealand and the UK. We run on HubSpot, three regional Maxio billing instances, our own product telemetry, JIRA and Totango - and we've spent the last year wiring AI into how the business actually operates, not as a pilot but as something people depend on every Monday morning.
The bottleneck now isn't the AI. It's the data feeding it. Our data function powers reporting and decision-making across the business - from ARR and customer health to product usage and customer-facing insights. We're modernizing our data platform, moving transformation logic into a version-controlled, tested, and governed framework built on dbt.
This is a foundational hire: you'll own the transformation and modelling layer end-to-end, turning raw source data into trusted, well-documented datasets that the whole business relies on.
You'll join a small, high-ownership data team and work directly with the Senior Manager of Data. This is a role for someone who wants to build the layer that matters, not maintain someone else's.
What you'll actually do
Document and version what exists.
A lot of how our data works currently lives in one person's head and in scripts that aren't in version control. Getting that into Git, into dbt models, and into runbooks is a core part of the first year.
Own the definitions.
One agreed meaning for ARR, churn, activation and pipeline stage - enforced in the data, not re-argued in every meeting. You'll maintain the definitions and the logic that implements them.
Own data quality.
Define the hygiene rules that matter - deal fields, subscription records, account mappings - build the automated checks that catch breaches, and chase the owners who need to fix them. This is as much a people job as a technical one.
Keep the plumbing running.
Own the daily data flows between our source systems and our reporting layer. Know what runs when, what depends on what, and what to do when something doesn't land. Fix it before anyone notices.
Maintain the AI connection layer. uses MCP (Model Context Protocol) servers to let AI tools query our business systems directly. You'll maintain these, extend them to new systems, and test what comes back. When an AI agent returns a number, you're the person who can say whether it's right and why.
Design and build our dbt project from staging through to business-ready marts - establishing the raw → staging → intermediate → marts layering, and the conventions the team builds on.
Translate business logic currently scattered across database views and materialized views into version-controlled, tested dbt models with clear lineage.
Define and own governed metrics - ARR (across our three regional Maxio instances), NPS, product usage, customer health - so a number means one thing across every report.
Build and maintain data quality tests, freshness checks, and documentation so issues are caught before they reach a dashboard, not after.
Partner with stakeholders in Finance, Customer Success, Sales, and Product to turn reporting needs into reliable, reusable datasets.
Support our Power BI reporting layer and contribute to new customer-facing dashboard capabilities as the platform matures.
Contribute to the broader platform modernization alongside the Senior Manager of Data
What you'll learn
This is an unusual role for someone early in their career, and it's worth being specific about why.
You'll see an entire business end to end.
Sales, billing, product usage, support, finance - in a company small enough that you can hold all of it in your head, and large enough that it's genuinely complex. Very few analysts get that view. Most spend years on one slice.
You'll get real hands-on AI infrastructure experience.
Not prompt-writing. The actual underlying components: MCP servers, agent tooling, retrieval, evaluating whether outputs can be trusted. The number of people worldwide with production experience of this is still small.
You'll learn modern data tooling properly.
dbt, version control, data quality testing, layered data modelling. We're also assessing a move to Databricks - if it proceeds, you'd be part of it.
You'll be visible.
You'll work directly with the BI Lead and with the CFO, whose remit covers Finance, RevOps, BI and AI implementation. There are no layers to disappear into.
You will be trained. You will not be left alone with a broken pipeline in month one.
What we're looking for
Essential:
Strong SQL - comfortable with joins, CTEs, complex transformations, window functions, and performance-aware query design against a relational warehouse.
Hands-on experience with dbt (or demonstrable readiness to own a dbt implementation), including modelling patterns, testing, and documentation.
Solid understanding of data modelling - dimensional modelling, layered architectures, and the discipline of turning messy source data into clean, trusted models.
Knowledge of AWS and Python for Data Engineering.
A software-engineering mindset applied to analytics - testing, modularity, code review, documentation as default practice.
Clear communicator who can work with non-technical stakeholders and translate business questions into data models.
Genuine attention to detail. This job rewards the person who notices that a total is $400 out and can't let it go
Willingness to chase people. A lot of data problems are fixed by getting a sales rep to update a field, not by writing code
Clear written communication - you'll be explaining to non-technical people why a number is what it is
Curiosity about how things actually work underneath
Nice to have:
Experience with Databricks platform
Experience with BI visualisation tools (Power BI particularly) and building for self-serve consumption.
Exposure to SaaS business metrics (ARR, retention, usage analytics) and subscription/CRM data (Maxio, HubSpot, or similar).
Experience with semantic layers, or with modernizing a legacy transformation layer (raw views/materialized views → a governed framework).
Experience in a small or sole-owner data team where breadth and self-direction matter.
AI native with hands-on work with LLM APIs or agent tooling
A background in data engineering, finance, operations or analytics in a SaaS business
We care more about how you think than what you've already used. If you've taught yourself something hard because you wanted to know how it worked, tell us about it.
Being straight with you
A meaningful share of this job is unglamorous. Data hygiene is repetitive before it's automated. If you want to build models, this isn't that role - but it is the role that makes those models possible, and it's the shortest path to understanding a business properly.
Some of what you inherit is messy. Scripts without version control, logic without documentation. Part of your job is fixing that, and it will be frustrating at times.
We're a lean team.
You'll have real ownership early, which means real responsibility early. That suits some people enormously and some not at all.
Where it leads The obvious progressions from here are analytics engineering, data platform work, or a commercial analytics path into RevOps or finance.
All three are open, and all three are strengthened by starting where the data actually comes from rather than where it gets presented.
Relevant Work Experience:
 Fluency in English communication is
A MUST
 Having experience in working with Excel
 Bachelor's degree and above in Statistics, Business, Economics, Data
Analytics, and other related fields
 Be opened minded, have the ability to learn fast and willing to do attitude
 Having experience in Business Intelligence is an advantage
 Knowledge of SQL, Power BI is a plus
BENEFITS
● Attractive salary: Nego. Performance based award.
● Young and dynamic working environment.
● Continuous development of hard and soft skills through work and professional training.
● Opportunity to approach newest technology trends.
● Salary reviews 1 times/year or on excellent performance
● Company's labor policy completely pursuant to labor legislation plus other benefits offered by the company (Company trip, Holiday, etc.)
CONTACT
ASTEK VIETNAM
Contact Person :
Ms. Dzung Nguyen
Phone Number
: [protected info]
Email
: [protected info]
Website
:
[protected info]?lang=en