Data Solutions Engineer

Establish the single source of truth for customer data. Then use it to get every customer to value, fast.

The Problem We're Solving

EviSmart™ has grown fast — data now lives in more places than it used to: the backend that runs the business, the CRM where sales and customer success work, and the data every lab brings with them when they join the platform. Reporting takes more cross-checking than it should, and the data-mapping step in lab onboarding — simple in concept but exacting in practice. This role exists to close that gap: one person who owns making EviSmart's customer data a single source of truth.

Why EviSmart

• 300 people. Two hubs: Vancouver HQ and Manila operations.
• 145% year-over-year SaaS growth — the market is responding.
• 28 countries. One platform. The dental industry's Autopilot.
• An in-house AI model research and development team building proprietary intelligence.

Why This Role, Right Now

Sales, Customer Success, and go-to-market teams need to open one system and trust what they see — today they can’t. And as the platform scales, onboarding has to become a repeatable machine instead of a per-customer rescue effort. These aren’t future problems; they’re live ones. The first months of this role are deliberately concrete: specific systems, specific deliverables, measured outcomes. But the mandate behind them is bigger — every system the platform adds and every AI capability the company builds will sit on the customer data foundation this person establishes.

What You’ll Own

Own — Reconciliation of customer data across systems: pull a clean customer list from the backend into the CRM — no duplicates, no ghost accounts — audit what’s already in HubSpot (call logs, deal history, contacts), and make the call on what stays, what gets cleaned, and what gets automated.

Build — A live two-way integration between HubSpot and the backend: data flows automatically, actions trigger from HubSpot, logic stays in the backend.

Establish — One trusted place to generate sales pipeline, retention rate, customer health, and all other sales, customer success, and go-to-market reporting.

Run — Customer onboarding data-mapping sessions directly with labs.

Deliver — A written brief for engineering: where labs hit friction during mapping, what’s causing it, and what to fix so labs can complete it independently.

Coordinate — With Customer Success on onboarding sessions and with our contracted HubSpot partner on integration.

Grow — Into the data foundation itself. As the early goals land, the scope expands toward owning and scaling customer onboarding as a repeatable machine — and toward the broader work of making EviSmart’s data trustworthy end to end, across every system it touches.

What You’ll Get

A foundation‑level seat: the data layer you build is what sales reporting, customer success, and the platform’s AI and analytics ambitions will run on.

Direct customer contact from week one — labs across 28 countries, real workflows, immediate feedback on whether your work holds up.

• Concrete goals with real gates: this role is defined by deliverables and measurable outcomes, not vague mandates.
• A role built to grow — the scope expands as the foundation you build proves out.
• Competitive compensation with salary range disclosed at offer.

How We Work

We ship before we’re 100% certain. We write things down because we have two offices and memory is lossy. We debate loudly and move without resentment. We treat the customer’s real problem as more important than an elegant internal process. If you’ve spent time waiting for permission to try something obvious — you’ll notice the difference here immediately.

The Question You’re Probably Asking

‘Is this a CRM admin job?’ No. The CRM is where the data problem surfaces first, so it’s the first proving ground — but the work is data engineering end to end: cleansing, integration, sync logic, reporting architecture, and reconciling what the backend, the CRM, and the customer’s own data each believe is true.

— EviSmart Talent Team

What We Need

• Bachelor’s degree in Statistics, Computer Science, Data Science, Economics, or a related field.
• 4–5 years of professional experience in data analysis, data engineering, or solutions/implementation engineering.
• Advanced SQL skills (joins, CTEs, window functions, subqueries).
• Proficiency in Python for data analysis and workflow automation.
• Experience with ETL/ELT pipelines and workflow automation.
• Hands‑on CRM data experience — auditing, cleansing, deduplication, and data migration. HubSpot strongly preferred.
• Systems integration experience — building or maintaining two‑way integrations between platforms (APIs, middleware, sync logic).
• Familiarity with Git/GitHub for version control and collaboration.
• Comfortable running customer‑facing working sessions and explaining data configuration to non‑technical users.
• Able to translate customer friction into clear, actionable briefs for engineering.

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