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 calibre engineering organisation 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

What We’re Looking For

Production data engineering experience. 3+ years building and operating production data pipelines or data platforms, including ownership of them once they are live
Strong SQL and modelling judgement. You model data for real consumers, and you find the source of truth before creating another copy of it
Strong command of Python, or comparable depth in another language used for data processing
Batch and event-driven processing. Experience with Kafka or an equivalent streaming system, a workflow orchestrator such as Airflow, Dagster or Prefect, and transformation tooling such as dbt or equivalent SQL-based workflows
Cloud warehouse, lake or lakehouse experience, and integrating data from external APIs, databases and files
Correctness under failure. Understanding of schema design, data contracts, idempotency, replay, backfills and late-arriving data, with automated validation and quality checks as standard practice
Operational ownership. You monitor production pipelines, investigate failures and turn incidents into better contracts, checks and design. You know when to improve the platform and when a simple pipeline is enough
Clear communication. You explain data models and technical decisions plainly, work directly with product, engineering and domain stakeholders, and challenge unclear definitions rather than encoding them

Preferred

Data engineering in financial services or another regulated domain
Financial advice, wealth management or investment data, including portfolios, transactions, holdings, valuations o

Data Engineer at Saturn