Note: The job is a remote job and is open to candidates in USA. Verifast is building a trust-focused platform for renters and property-related applications. The Data Engineer will own Snowflake-based data infrastructure, including ingestion pipelines, data models, semantic definitions, quality monitoring, and commercial datasets supporting marketing, sales, customer success, finance, and client-facing analytics.
Responsibilities
- 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
- 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
Skills
- • 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
- • Python preferred
- • 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
Benefits
- Full-time, hybrid or remote work arrangement
Company Overview