Data Engineer

Verifast; United States; Hybrid; USD 58,000 - 87,000; Full time

Job summary

Verifast is hiring a Data Engineer to own platform and business data, building robust pipelines in Snowflake and ensuring trusted numbers for boards, clients, and invoices. You’ll partner with engineering and product teams to deliver analytics at scale, with a focus on data quality and observability.

You will work cross-functionally with marketing, sales, customer success, and finance, owning the data definitions and the lineage that drives accurate reporting and billing insights.

Qualifications

• 4–7 years in data engineering or analytics engineering with real ownership of production pipelines and models.
• Strong Snowflake experience—modelled in it, tuned it, and managed cost and access.
• Advanced SQL and Python; you can build a pipeline, not only transform inside the warehouse.
• Experience with modern data tooling such as dbt or an equivalent transformation framework, orchestration, and CI.
• Commercial fluency—experience with marketing and sales data and CRM platforms.

Responsibilities

• Design and build data models in Snowflake from raw to reporting layers.
• Own and extend ingestion pipelines from application databases into the warehouse, including CDC paths.
• Collaborate with Marketing and Sales to build datasets that answer business questions.
• Create and maintain reporting foundations: funnels, attribution, revenue signals, and KPI definitions.
• Ensure data quality, tests, monitoring, and lineage documentation for trust and reuse.

Skills

Snowflake

SQL

Python

dbt

CI/CD

Tools

Estuary

Job description

Verifast is building a future where trust follows you–and never has to be earned twice.

Powered by experience, we lead with empathy and know how to include what others overlook, keeping the process human where it counts.

We build trust transparently, turning it into a currency that gets approvals faster and levels the playing field for those who do things right.

For renters who work hard, dream big, and deserve to be seen as more than a number.

Mission of the Role:

Every application that runs through Verifast produces decisions, verifications and outcomes —

and almost every question worth asking about this business runs through that data. How many

applicants complete. Where they drop. Which verification paths are accurate. What a client is

actually using and should be billed for. Which campaigns bring in accounts that stay.

We're hiring a Data Engineer to own those answers. This is a hands‑on individual-contributor

role sitting between the platform and the business. You'll build the pipelines and the models in

Snowflake, and you'll be accountable for whether the numbers reaching a board deck, a sales

conversation or a client invoice can be trusted.

The Data Engineer owns two things in equal measure:

1. Platform data — the ingestion, models and warehouse standards that turn what the

product emits into something the business can reason about, and the tests and

monitoring that keep it honest.

2. Business data — the marketing, sales, usage and billing datasets that let commercial

teams answer their own questions, with one agreed definition per metric.

You will work directly with the teams that depend on this data, and the outcome we want looks

different for each. With engineering, it is analytics designed into the product at the source —

event contracts agreed before they ship, so data arrives correct rather than being reconstructed

later. With marketing, it is attribution that shows which channels bring in accounts that stay, not

just accounts that sign. With sales, it is pipeline and account health grounded in real product

usage, so a renewal conversation starts from evidence. With customer success and finance, it is

usage and billing data accurate enough to put in front of a client. Across all of them, it is one

agreed set of numbers instead of four.\

What You’ll Own:

• The warehouse — our Snowflake environment: the models, the layers between raw and reporting, and the standards that keep them coherent as the platform changes underneath them.
• Ingestion and change data capture — the pipelines that move data out of the product and into the warehouse, and the reliability of that movement. A pipeline that silently ingests less than it should is the failure mode that matters most, and it is yours to detect.
• The semantic layer — the definitions of the metrics the company runs on: completion, drop-off, verification accuracy, usage, activation, retention. One definition per metric, documented, and the same number wherever it appears.
• Marketing and sales data — campaign and attribution data, funnel and pipeline reporting, account health, and the joins between commercial systems and product usage that show whether a customer is actually succeeding.
• Usage and billing data — the datasets behind consumption reporting and invoicing.
• These are revenue‑affecting and are held to a higher bar than analytics.
• Data quality and observability — tests, freshness checks and alerting on everything you own, so a break is found by us rather than by whoever is reading the dashboard.
• Self‑serve enablement — the models, documentation and access patterns that let marketing, sales, customer success and product answer their own questions without an engineering ticket.
• Partnership with engineering — working with backend engineers on what the product emits, so analytics is designed in rather than reconstructed afterwards.

What You’ll Do:

• Design and build data models in Snowflake — raw through to reporting layers — that an analyst can understand and a team can build on.
• Own and extend the ingestion pipelines from the application database and third‑party systems into the warehouse, including the change data capture path.
• Sit with marketing and sales, understand the question behind the request, and build the dataset that answers it rather than the extract that was asked for.
• Build the reporting leadership runs on: funnel and conversion, verification performance, client usage, retention and revenue signals.
• Instrument and validate product events with engineering — agreeing the schema before it ships, and holding the line on it afterwards.
• Write tests and monitoring for the data you own, and treat a silent under‑count with the same seriousness as an outage.
• Reconcile the numbers when two systems disagree, then fix the reason they disagreed.
• Document metric definitions and model lineage so the business can trust and reuse them.
• Support client‑facing analytics, where the audience is a property manager or underwriter rather than an internal user.
• Manage cost and performance in the warehouse as volume grows — query patterns, storage and compute.

Qualifications & Experience:

• 4–7 years in data engineering or analytics engineering, with real ownership of production pipelines and models.
• Strong Snowflake experience — you have modelled in it, tuned it, and managed cost and access in it, not just queried it.
• Advanced SQL and a working programming language — Python preferred. You can build a pipeline, not only transform inside the warehouse.
• Modern data tooling — dbt or an equivalent transformation framework, orchestration, version control and CI. We're tool‑agnostic beyond that if you can show the rigor.
• Commercial fluency — you have built for marketing and sales, understand attribution, funnel and pipeline mechanics, and can work with a CRM and a marketing platform as source systems.
• Engineering partnership — you have worked alongside application engineers on event design and schema change, and can hold a technical conversation about the system producing your data.
• Data quality discipline — testing, monitoring and reconciliation are part of how you build, not something added afterwards.
• Strong written and verbal communication — you can explain a metric definition to a sales leader, a modelling trade‑off to an engineer, and a caveat to an exec, in the right language for each.

Nice‑to‑Haves:

• Experience with change data capture tooling — Estuary, Fivetran, Debezium or similar.
• Background in fintech, lending, underwriting, proptech or another regulated domain.
• Experience building usage‑based billing or consumption datasets.
• Exposure to customer‑facing or embedded analytics.
• Familiarity with data governance in a regulated environment — PII handling, retention, access control, SOC 2.

Role is reporting to the VP of Engineering

Verifast is Head Quartered in Toronto, Ontario

Role is full-time, Hybrid or Remote

Salary is based on experience $80,000-$120,000