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Northbeam · 1 month ago

Senior Software Engineer, Data Systems (Python)

United States

Full-time

Remote

Senior Level

$170K/yr - $200K/yr

5+ years exp

Northbeam is building the world’s most advanced marketing intelligence platform, providing top eCommerce brands a unified view of their business data. As a Senior Software Engineer in Data Systems, you will design and implement scalable data pipelines and maintain APIs to ensure reliable data integration across various platforms.

Information TechnologyMachine Learning

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

Qualification

PythonETLAPI developmentSQLBigQueryAirflowDockerKubernetesOAuth 2.0GraphQLWebhooksData governanceGDPR complianceSOC2 compliance

Required

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