Software Engineer, Data Systems (Python)

Northbeam

1d•$140,000 - $155,000•Remote

About The Position

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. Northbeam is fundamentally a data product - the whole company. We don’t sell shoes, ads, games, or database technologies. We sell data: quality integrations with a variety of platforms, fresh and reliable data pulls, correct aggregations, and algorithmic insights on top of that data, all packaged up in a user-facing application. What this means is that out data systems team is foundational and load-bearing. As a data-tilted software engineer at Northbeam, you'll work alongside product managers, product engineers, and business leaders to turn customer needs into reliable data pipelines and products. You'll be supported by engineers who have built these systems before, with room to take on more ownership as you grow into the team. The work involves building, maintaining, and improving a labyrinth of integrations and transformations across a complex network of touchpoints. The system is powered by data spanning numerous ad platforms, a variety of order management systems (Shopify, Amazon, and others), and our own real-time events collected as customers navigate their online stores. Curiosity, a bias toward shipping, and a real appetite for building data pipelines at scale are what matter most here.

Requirements

3+ years of experience in data engineering, software engineering, or integration engineering, with exposure to ETL, APIs, or data pipeline work.
Solid working proficiency in Python.
Experience building against REST APIs; familiarity with GraphQL or webhooks is a plus.
Comfort with SQL and some exposure to a cloud data warehouse (BigQuery, Snowflake, Redshift, or similar).
Some exposure to orchestration frameworks such as Airflow, Dagster, or Prefect.
Willingness to work in a containerized environment (Docker).
A track record of shipping steadily and asking good questions when the path isn't obvious.

Nice To Haves

Experience implementing authentication flows (OAuth 2.0, API keys, secrets management).
Familiarity with Kubernetes or other production deployment tooling.
Experience working with ERP systems, CRMs, CDPs, or other enterprise data tools and their APIs.
Exposure to event-driven architectures and real-time data processing tools.
Awareness of data governance and compliance considerations (GDPR, SOC 2).
Experience in a multi-tenant SaaS or data-intensive environment.

Responsibilities

Build and maintain data pipelines that ingest and transform data from a variety of sources, with an eye toward reliability and maintainability.
Develop and support APIs that enable secure, tenant-aware data integrations with external systems.
Work within event-driven and batch processing architectures to keep data fresh and consistent.
Contribute to API design and integration patterns that support both real-time and batch ingestion across different authentication mechanisms (OAuth, API keys, etc.).
Add monitoring and alerting that surfaces data freshness issues, failures, and performance problems before customers notice them.
Improve existing data flows and transformations, weighing cost, efficiency, and speed of delivery in a cloud-native environment.
Partner with data engineering, infrastructure, and product teams to make our integration platform easier to extend and onboard new sources into.