Data Engineer

Your role

As a Data Engineer, you will build the data foundations that Saturn’s products, AI systems, operations, and decision-making depend on.

Saturn is a Series A, Y Combinator-backed company building the AI-native operating system for financial advice. Our platform combines a living data model of the client, AI agents that complete complex advice and operational workflows, and compliance logic embedded directly into how work is produced. All of that rests on data drawn from CRMs, platforms, providers, and product systems, which arrives inconsistent, incomplete, and rarely defined the same way twice.

You will not treat a pipeline as finished because the data reached its destination. You are expected to understand what the data means, how it changes, where quality is lost, and how consumers can tell whether to trust it. A pipeline that runs cleanly and produces misleading data has failed. Your work determines whether Saturn can use its growing volume of financial and operational data consistently across product, reporting, and AI.

The Team

Our engineers care deeply about craft, speed, and quality. They include early and founding team members from companies including Rippling, Postman, Gojek, CRED, and Slice.

You will work alongside product designers, backend engineers, AI engineers, and domain experts with decades of experience in financial advice and compliance.

We are building a small, high-caliber engineering organization for people who want genuine ownership, difficult product problems, and the opportunity to shape an important company while its foundations are still being formed.

What You’ll Work On

• Ingestion from financial platforms, CRMs, providers, and internal services, across Kafka-based streams, event-driven movement, and batch pipelines for large or scheduled imports
• Core data models covering clients, households, firms, assets, products, advice, and evidence, turning raw source data into clear, reusable datasets rather than another copy
• Data contracts and schema evolution between producers and consumers, so schema changes do not silently break downstream systems
• Validation, reconciliation, and quality monitoring at the boundaries that matter, including freshness and completeness checks that catch problems before consumers do
• Lineage, provenance, and auditability for regulated and evidence-heavy workflows, keeping material transformations visible, testable, and explainable
• Pipelines built for reality: retries, replay, backfills, late-arriving data, and partial failure treated as expected operating conditions rather than exceptions
• Datasets for Saturn’s AI and retrieval systems, alongside trusted data for product reporting, operations, and business analysis
• Access control, retention, and handling of personal and financial data, plus the query performance, storage efficiency, and cost of the platform as volume grows