[Hiring] Data Engineer @Euler

Role Description

As our Data Engineer, you will own the health of EULER’s data end to end — how it is captured, how it is validated, how it is protected, and how it is structured so the business can rely on it. This is a foundational role. Partner ecosystems are a genuinely hard data domain: every customer brings their own CRM, their own vocabulary, and their own way of defining what counts as a qualified opportunity. Turning that into something consistent and trustworthy is the work, and it is what makes everything downstream possible. You will set the standard for how we treat data, and own it.

Key Responsibilities

• Data capture: Define what we instrument and why, and work with engineering and product to get it recorded well the first time.
• Data quality and integrity: Build the validation and reconciliation that let us stand behind every number we report.
• Privacy and compliance: Own our data protection and anonymization practices, and keep our commitments to customers clear, documented and defensible.
• Data modelling and structure: Design the canonical models and taxonomies that make data consistent across customers.
• Analytics enablement: Build the datasets and documentation behind our reporting, so teams and customers get numbers they can act on.
• Documentation: Make the data understandable to people who did not build it.

Qualifications

• Strong SQL — window functions, careful aggregation, and the instinct to check a distribution before trusting a mean.
• Real experience owning production data pipelines end to end.
• Enough software engineering to write, test and review pipeline code. Tested, reviewable code matters more to us than any particular language.
• Scepticism as a working habit. “That number looks wrong, and here is why” is the most valuable sentence you can say here.
• Comfort owning a domain independently and setting your own priorities.
• Clear written communication.
• Professional working English.
• Availability during US East Coast working hours.

Nice to Have

• Data privacy in practice — anonymization and pseudonymization, GDPR/LGPD applied to real datasets.
• Multi-tenant B2B SaaS data.
• Analytics engineering practice (dbt or equivalent) with tests as a first-class artifact.
• Statistics good enough to know when a model is the wrong answer — survival analysis, censoring, selection bias, hierarchical models.
• Cloudflare Workers and Hyperdrive.
• Postgres, Parquet, or DuckDB.
• Experience with Bubble or similar low-code platforms, and their constraints.
• You do not need deep learning, an LLM portfolio, or a PhD.

Benefits

• The opportunity to be part of an exciting startup journey, contributing directly to company growth.
• Full ownership of your domain — you define how it works.
• A remote working environment with flexibility to work from anywhere.
• A competitive salary.
• A supportive and innovative team culture where your ideas and contributions are valued.
• Opportunities for professional development and growth.

How to Apply

Along with your application, tell us briefly about a dataset you did not trust: how you found out, and what you did about it. We are more interested in that than in your CV. We will not ask you for unpaid project work.