Hire Data Engineer for Fintech Startups
Senior, direct, no layers

Most fintech startups businesses do not need more dashboards. They need the unglamorous work underneath: data pulled from each source on a schedule, checked for errors, modelled into consistent definitions, and delivered to one reporting layer the team can trust.

The shape of the data decides the shape of the build. The product is the funnel: signups, identity checks, first transactions, and the moment a user either becomes active or never returns. Sources, models, and dashboards follow from that, not from a standard template, which is why two businesses in the same industry still end up with different pipelines.

Handover is part of the deliverable: documented models, scheduled pipelines the team can monitor, and dashboards the team can extend. The business should not need the person who built it to keep the numbers flowing.

The warehouse draws on KYC and onboarding outcomes, support tickets, billing and revenue records, product analytics events, and transaction records. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so signup-to-active conversion by channel, drop-off by onboarding step, and activation and retention cohorts all read from the same definitions instead of separate extracts.

Most fintech startups businesses already run on product analytics (funnels and retention), CRM (leads and onboarding), payment rails (transactions), compliance tools (KYC and reporting), and support software (tickets and chats). The pipeline work starts from those systems as sources, not by replacing them, and lands the cleaned data in a warehouse the business owns, with dashboards on top for the team.

A sensible first project is one pipeline done properly, often KYC and onboarding outcomes feeding signup-to-active 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 KYC and onboarding outcomes. 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 signup-to-active conversion by channel is a number the team trusts instead of a spreadsheet they debate.

product analytics handles funnels and retention. CRM handles leads and onboarding. payment rails handles transactions. compliance tools handles KYC and reporting. support software handles tickets and chats.

The realistic outcome is a weekly meeting run on trusted numbers: signup-to-active conversion by channel and drop-off by onboarding step arrive on schedule, definitions are no longer debated, and signups dropping at identity checks becomes visible early enough to act on. That is the standard this engagement is measured against, not a dashboard launch.

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 fintech startups team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.