Senior Software Engineer, Data Systems (Python)

Northbeam

3h•$170,000 - $200,000•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. 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 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 working at Northbeam, you will work with a cross-functional team of product managers, product engineers, and business leaders to translate our customers’ feedback into scalable data pipelines and products. The work involves creating, maintaining, and improving a labyrinth of integrations and transformations in a complex network of touchpoints to keep everything running smoothly. The system is powered by data that spans numerous ad platforms, a variety of order management systems (such as Shopify and Amazon), as well as our own real-time events that we collect as our customers navigate their online stores. Curiosity, experience, and a desire to build data pipelines and applications at scale will be the key to success in this role.

Requirements

5+ years of experience in data engineering, software engineering, or integration engineering, with a focus on ETL, APIs, and data pipeline orchestration.
Strong proficiency in Python
Experience with API-based ETL, handling REST, GraphQL, Webhooks
Experience implementing authentication flows
Proficiency in SQL and BigQuery
Experience with orchestration frameworks (e.g., Airflow) to manage and monitor complex data workflows.
Familiarity with containerization (Docker, Kubernetes) to deploy and scale workloads.
Ability to drive rapid development while ensuring maintainability, balancing short-term delivery needs with long-term platform stability.

Nice To Haves

Detailed understanding of authentication mechanisms (OAuth 2.0, API keys, secrets management) and secure multi-tenant architectures.
Experience working with ERP systems, CRMs, CDPs, or complex other enterprise data tools and their APIs.
Exposure to event-driven architectures and real-time data processing tools
Knowledge of data governance, compliance (GDPR, SOC2), and security best practices for handling sensitive data.
Experience working in a multi-tenant SaaS or large-scale data-intensive environment.

Responsibilities

Design and implement scalable, high-performance data pipelines to ingest and transform data from a variety of sources, ensuring reliability, observability, and maintainability.
Build and maintain APIs that enable flexible, secure, and tenant-aware data integrations with external systems.
Work with event-driven and batch processing architectures, ensuring data freshness and consistency at scale.
Drive clean API design and integration patterns that support both real-time and batch ingestion while handling diverse authentication mechanisms (OAuth, API keys, etc.).
Implement observability, monitoring, and alerting to track data freshness, failures, and performance issues, ensuring transparency and reliability.
Optimize data flows and transformations, balancing cost, efficiency, and rapid development cycles in a cloud-native environment.
Collaborate with data engineering, infrastructure, and product teams to create an integration platform that is flexible, extensible, and easy to onboard new sources.