Hire Data Engineer for SaaS Companies
Senior, direct, no layers

A saas companies business usually has the data it needs, but not in one place. Product analytics events, CRM pipeline data, and billing and subscription records live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When trials that never activate keeps happening, the cause is rarely a lack of data; it is that the data never gets collected, cleaned, and presented in a form anyone can rely on.

The engagement covers software subscriptions sold to teams and businesses as a data problem: what the business measures, where that data lives, and what decisions it should support. The company runs on the funnel and the product: trials started, activations earned, seats expanded, and churn that shows up in usage weeks before it shows up in revenue. Reporting built on that reality gets used in weekly meetings; reporting built on generic metrics gets ignored.

The work is done inside the business's own systems, with definitions agreed before any dashboard is built. A metric means the same thing in every report, because the model defines it once. That discipline is the difference between reporting the team trusts and reporting the team argues with.

The warehouse draws on product analytics events, CRM pipeline data, billing and subscription records, support tickets, and usage and session data. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so trial-to-paid conversion by channel, activation rates by onboarding step, and churn cohorts and leading indicators all read from the same definitions instead of separate extracts.

The existing stack here usually means product analytics (trials and usage), CRM (pipeline), billing software (subscriptions), support software (tickets), and email platform (onboarding and campaigns). Each of those systems holds part of the picture. The build connects them into one modelled layer, so a report draws from the warehouse rather than from five separate exports.

A sensible first project is one pipeline done properly, often product analytics events feeding trial-to-paid conversion by channel, with the team using the resulting report for real decisions before anything else is added. A pipeline that proves itself on one decision earns the trust to carry the rest.

Take product analytics events. Today it probably arrives as an export someone cleans by hand. In the pipeline it is pulled on a schedule, checked for missing or duplicate records, and modelled into the same definitions every report uses, so trial-to-paid conversion by channel is a number the team trusts instead of a spreadsheet they debate.

product analytics handles trials and usage. CRM handles pipeline. billing software handles subscriptions. support software handles tickets. email platform handles onboarding and campaigns.

What changes in practice is where numbers come from. When product analytics events and CRM pipeline data flow through tested pipelines into one modelled layer, the team stops rebuilding reports and starts acting on them. The time saved is real, but the trust is the actual deliverable.

That also changes how the business argues: disagreements move from whose spreadsheet is right to what the number means and what to do about it, which is where a saas companies team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.