Introduction
Holistics is a Business Intelligence platform built for the agentic AI era. We are the team behind the BI tool Holistics, the database diagram tool dbdiagram.io, and the open-source project dbml.org. We help data teams build a trusted, supervised self-service analytics experience for their business stakeholders, so data teams free themselves from low-level ad-hoc requests while business users get data quickly and reliably.
We're searching for an Analytics Engineer who's equally happy modelling our own internal data and solving a prospect's data problem live on a call. This is a genuinely hybrid role, split about evenly: roughly half your time is internal analytics-engineering that keeps our own data stack sharp, and half is presales / solutions work that helps win customers. You're someone who enjoys both sides and moving between them.
This is a full-time role based in Ho Chi Minh City, Vietnam, with a flexible hybrid WFO/WFH setup.
What you will do
This role is a hybrid of technical analytics work and customer-facing engagements. Your time will be allocated across two core functions.
1. Internal data & analytics engineering
- Contribute to our internal data pipeline: Help maintain our ingestion, transformation, and reporting pipeline so internal stakeholders (finance, product, support ops) get timely, correct data. Doing this hands-on keeps you fluent in the product and feeds real feedback to our product team.
- Build best-practice data models: Develop and refactor internal models in dbt using dimensional modelling (Kimball/Inmon, star/snowflake) — these double as real-world examples for customers.
- Keep data trustworthy: Add tests and validation so internal metrics are correct, and translate those practices into customer-facing guidance.
- Build with coding agents & dogfood: Develop our internal models and dashboards using AI coding agents (Development Copilot, or Claude Code / Cursor in your IDE) on our analytics-as-code layer — then feed what you learn back to product and marketing, and test-drive emerging tools that could sharpen our stack or demos.
2. Presales & solutions (customer-facing)
- Craft killer demos: Slice a public dataset, model it in Holistics' analytics-as-code layer, and polish a dashboard that makes prospects say "aha!" during a live call.
- Run technical POCs: Work hands-on with prospects on their own data to prove Holistics solves their problem — schema reviews, modelling, and answering deep technical questions.
- Support RFPs & technical Q&A: Pair with Sales to answer deep questions (performance, workload isolation, modelling approach), then drop polished answers into our reusable library.
- Partner with the sales reps: Join solutioning calls alongside reps; you own the technical/data substance, they drive the commercial relationship.
- Build enablement assets: Turn recurring prospect/customer questions into demo warehouses, sample repos, step-by-step guides, and short videos so the next deal moves faster.
The two sides ask for different working styles: presales is time-sensitive and engagement-heavy, while internal data work needs longer stretches of uninterrupted focus. We've shaped the role so each gets its own protected time, rather than constantly interleaving the two.
Why this role is unique
A few things make this more than a standard analytics-engineering job:
- You'll build BI with AI coding agents. Holistics is at the forefront of agentic BI. Because the whole platform is analytics-as-code on a semantic layer, AI agents (our cloud Development Copilot, or Claude Code / Cursor) develop models, metrics, and dashboards natively. For us this is just how the work gets done now, not a pilot. And since we make the product, how you work feeds straight into where it goes next.
- More than two crafts — it's product building. You build with the product internally and put it in front of prospects, which drops you into a tight feedback loop: build and use it, demo it and hear where it hurts, then feed that back into making it better. You'll often catch what's broken before the product team does, because you're the one hands-on with it and feeling the customer's pain. Do this for a while and you grow real product sense, on top of becoming a stronger engineer who's also at ease with customers.
- A BI vendor that practices what it preaches. We wrote the book on setting up analytics and we live by our worldview: metric-centric, self-serve, no dashboard sprawl. You'll work alongside people who think hard about the craft.
- Your work gets used. The demos you build help close deals; the data you model is what the company runs on. Small team, so nothing you make sits on a shelf.
Our current data setup
We wrote an entire book on the analytics landscape, so it's fair to say we know a thing or two about data stacks. Ours:
- Ingestion & Transformation: Event tracking (Snowplow) and ETLs from various sources into our data lake (GCS) and warehouse (BigQuery), transformed into the data mart via dbt.
- Serving: Reporting on Holistics; reverse-ETLs push data back into our CRMs for operations.
- Orchestration: Prefect orchestrates and automates the pipelines and other operations.
On the business side we practice what we preach: instead of building one-off reports, the Data team maintains a common set of metric definitions and educates business users to self-serve.
What we look for in you
A blend of technical depth and communication skills, as you'll be in front of prospects and in the data.
Mindset:
- Genuine interest in data analytics / BI and a love of owning the whole data stack, not one slice.
- Customer-facing energy: Discovery calls, demos, and live problem-solving energize you more than they drain you. You're comfortable thinking on your feet in front of a prospect.
- Problem-solving & critical thinking: You break ambiguous problems down and reason from first principles. That's also what lets you think in systems — you see how business operations, data collection, and architecture connect, and once you solve something you template it for the next.
- Good communication in English (written and spoken) — you write like you code: clear, concise, maintainable.
- Continuous learner with a growth mindset.
Technical skills (the data-engineering foundation):
- SQL: Solid foundation — readable queries to extract/manipulate data; you can explain grain and joins.
- Data modeling: Familiar with dimensional modeling and star/snowflake schemas, and why we shape raw data into user-friendly models.
- Programming: Some Python (or equivalent) for scripting and data manipulation.
- Version control: Comfortable with Git basics.
- Cloud data warehouse: Exposure to BigQuery, Snowflake, Redshift, or similar (academic or work).
Experience: This is a junior-to-mid role — we hire on a strong technical foundation plus the aptitude and appetite for customer-facing presales work, and grow you in both aspects. Prior exposure to solutions/presales, consulting, or customer-facing technical work is a plus, not a requirement.
Nice-to-haves: Hands-on dbt, Looker/LookML, Holistics or other modern BI tools; experience building demo environments or enablement assets; a demonstrated interest in how data infrastructure drives business decisions.
Interview Process (≈ 3 weeks)
- Round 1: A chat with the team and a light technical interview — your skills, experience, and past projects.
- Round 2: A short take-home or on-site assignment to assess technical and problem-solving skills.
- Round 3: A culture and behavioural chat with management.
Why You'll Love Working
💕 Benefits:
- Flexible working arrangement, hybrid work (WFO+WFH) policies
- 14 annual leaves, 14 sick leaves, and childcare leave policy.
- Annual budget for personal and professional growth, well-being, and interest cultivations.
- 24/7 healthcare insurance; periodic medical checkups.
- 13-month salary; annual compensation reviews
- Weekly happy hours, company-organized events.
🏢 Workstations provided for maximum productivity
- MacBook (or Laptop) Grant
- Mechanical Keyboard + Mouse
- Big-screen monitor(s)
🌐 Working time: From Monday – Friday, from 9.00 a.m. to 6.00 p.m.
🌐 Work location: 457-459 Nguyen Dinh Chieu, Ward 5, District 3, Ho Chi Minh City, Vietnam.
