Senior Backend Engineer, Data Pipelines and Integrations

About the Role

We are looking for a Senior Backend Engineer to own the systems that transform our unified dataset into application-ready content and robust downstream analytics. This role bridges the gap between core datasets and end-user experiences by building the pipelines, services, and models that make our content discoverable, searchable, analyzable, and operationally reliable.

Your work will power both the product and the business: organizing content into application-ready structures; managing fine-grained usage events; building ETLs that support reporting, billing, and analytics; and developing fingerprinting pipelines for deduplication, rights attribution, and safety. You will architect systems that ensure our data remains consistent across ingestion, application surfaces, and downstream consumers.

You will collaborate closely with Product, ML Research, Analytics, and Infrastructure teams, working with tools such as BigQuery, Dataflow/Beam, PubSub, and internal microservices. Experience designing data models that support real-time features, retrieval, and analytics is strongly valued.

What You’ll Do

• Design and maintain application-level data models that organize rich content into canonical structures optimized for product features, search, and retrieval.
• Build high-reliability ETLs and streaming pipelines to process usage events, analytics data, behavioral signals, and application logs.
• Develop data services that expose unified content to the application, such as metadata access APIs, indexing workflows, and retrieval-ready representations.
• Implement and refine fingerprinting pipelines used for deduplication, rights attribution, safety checks, and provenance validation.
• Own data consistency between ingestion systems, application surfaces, metadata storage, and downstream reporting environments.
• Define and track key operational metrics, including latency, completeness, accuracy, and event health.
• Collaborate with Product teams to ensure content structures and APIs support evolving features and high-quality user experiences.
• Partner with Analytics and Research teams to deliver clean usage datasets for experimentation, model evaluation, reporting, and internal insights.
• Operate large analytical workloads in BigQuery and build reusable Dataflow/Beam components for structured processing.
• Improve reliability and scale by designing robust schema evolution strategies, idempotent pipelines, and well-instrumented operational flows.

What We’re Looking For

• Experience building ETL/ELT pipelines, event processing systems, and structured data models for applications or analytics.
• Strong background in data modeling, metadata systems, indexing, or building canonical representations for heterogeneous content.
• Proficiency in Python, SQL, and scalable data-processing frameworks (Dataflow/Beam, Spark, or similar).
• Familiarity with BigQuery or other analytical data warehouses and strong comfort optimizing large queries and schemas.
• Experience with event-driven architectures, Pub/Sub, or Kafka-like systems.
• Strong understanding of data quality, schema evolution, lineage, and operational reliability.
• Ability to design pipelines that balance cost, latency, correctness, and scale.
• Clear communication skills and an ability to collaborate closely with Product, Research, and Analytics stakeholders.

Nice to Have

• Experience building application-facing APIs or microservices that expose structured content.
• Background in information retrieval, indexing systems, or search infrastructure.
• Experience with fingerprinting, perceptual hashing, audio similarity metrics, or content-matching algorithms.
• Familiarity with ML workflows and how downstream analytics and usage data feed back into research pipelines.
• Understanding of batch + streaming architectures and how to blend them effectively.
• Experience with Go, Next.js, or React Native for occasional full-stack contributions.

Why Join Us

• You will design t