Staff Software Engineer, Data Systems (Python)
About Northbeam
Northbeam is building the world's most advanced marketing intelligence platform, providing top eCommerce brands a unified view of their business data through powerful attribution modeling and customizable dashboards. Our technology helps customers accurately track ad spend, understand the full customer journey, and drive profitable growth. Machine learning has been at the heart of Northbeam since day one: our full suite of measurement, spanning multi-touch attribution, MMM+, and incrementality, is built on it, along with laser-accurate first-party data. Now we're taking that foundation further, using AI to help brands operationalize their data and turn Northbeam insights into automated decisions.
We're experiencing rapid growth, have strong product-market fit, and are looking for the right people to help us scale. This is a rare chance to make a meaningful impact at a fast-moving, high-growth company. At Northbeam, you'll join a team of driven, collaborative, and talented individuals who value personal growth and excellence. We'd love for you to be part of our journey.
We're a remote-friendly company with physical offices in San Francisco and Los Angeles.
About the Role
Northbeam is fundamentally a data product - the whole company. We don’t sell shoes, ads, or games. We sell data: quality integrations with a variety of platforms, fresh and reliable data pulls, robust data ingest APIs, correct aggregations, and algorithmic insights on top of that data, all packaged up in a user-facing application.
What this means for you is that high-quality, robust data integration is at the core of what we do, and your work will have a direct connection to the company’s success.
Our data systems team is foundational and load-bearing, and this role sets its technical direction. As a Staff engineer , you'll own the architecture of the integration and ingestion platform that the rest of the company builds on — deciding how we onboard new sources, where the abstractions live, and what "reliable" means in practice. You'll work across product, engineering, and with business leaders, and your judgment will shape what the team builds long after any individual project ships.
The work involves a labyrinth of integrations and transformations across a complex network of touchpoints, powered by data spanning numerous ad platforms, order management systems (Shopify, Amazon, and others), and our own real-time event collection.
Your Impact
- Own the architecture of our ingestion and integration platform, setting the patterns that determine how quickly and safely we onboard new data sources.
- Define technical direction for event-driven and batch processing across the company, and make the build/buy and migration calls that go with it.
- Establish the standards for observability, data freshness, and failure handling that the data systems team works to — and hold the bar on them.
- Lead design across team boundaries, working with data engineering, infrastructure, and product to keep platform decisions coherent.
- Raise the ceiling of the engineers around you through design review, mentorship, and the code you write.
- Identify the platform problems nobody has scoped yet, make the case for solving them, and drive them to completion.
What You Bring
- 8+ years of software or data engineering experience, including significant time owning the architecture of large-scale ingestion, ETL, or integration platforms.
- Deep proficiency in Python, and strong SQL and cloud data warehouse experience (BigQuery or equivalent).
- A track record of designing API-based ETL systems at scale — REST, GraphQL, webhooks, and the authentication and multi-tenancy concerns that come with them.
- Production depth with orchestration (Airflow or similar) and containerized infrastructure (Docker, Kubernetes).
- Demonstrated technical leadership: setting direction, aligning engineers who don't report to you, and improving how a team builds, not just wh
This role requires you to be in the United States. If that means relocating or flying in, it is worth checking fares before you commit to a start date.
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