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Senior Data Engineer

EngineeringFull-timeNew York or Remote

What we do

The events that move markets and the data that trains models both happen in the real world first - long before they show up in a packaged vendor dataset. EdgeOrigin closes that gap: our edge nodes listen to real-world events where they occur and deliver evaluated records with the provenance a research desk or a training pipeline can trust.

Our customers are hedge funds that trade on our signal and AI teams that train on our data. The technical work spans global event listening, low-latency evaluation, point-in-time history, and regional delivery, with production responsibility from ingestion through client delivery.

About this role

As a Senior Data Engineer, you'll own the pipeline at the heart of the product - the edge network that listens to real-world events worldwide, normalizes and timestamps each observation at the receiving node, and streams evaluated records to the desks and models that consume them. When latency, freshness, or reliability slips, customers feel it immediately, so this is work where the details matter.

You'll own that pipeline end to end: designing the global listening layer, building the streaming and bulk-delivery paths, keeping point-in-time history honest, and carrying provenance through every stage. It's demanding, high-scale infrastructure work with a direct line to the value customers pay for - fresh, clean, trustworthy data.

What you'll do

  • Design and operate the global edge listening layer - node deployment and ingestion across many sources, running continuously.
  • Build the streaming and bulk-delivery paths that get data to customers with minimal added latency.
  • Keep the data honest: point-in-time history, deduplication, and provenance carried end to end.
  • Make it reliable at scale - backpressure, retries, and clean recovery when a source misbehaves.
  • Partner with the ML team so the listening layer hands the models clean input.
  • Watch the pipeline in production and catch freshness or quality regressions before customers do.

What we're looking for

  • 5+ years building and operating data-intensive systems in production, with real ownership of pipelines that served live traffic.
  • Strong Python and SQL, plus fluency with a streaming stack (Kafka, Flink, or similar) and modern data tooling (Spark, Arrow, Airflow).
  • Experience with high-volume ingestion and web-scale collection - and the reliability problems that come with it.
  • Comfortable with the production stack: containers, Kubernetes, CI/CD, and a major cloud (AWS or GCP).
  • A real feel for latency and the slow tail, not just average throughput.
  • Comfortable owning open-ended production problems on a small team with broad technical scope.

Nice to have

  • Experience with market data, alternative data, or other latency-sensitive data.
  • Background in geospatial or satellite-imagery pipelines.
  • Lower-level systems chops (Rust, Go, or C++) for the moments where milliseconds matter.

This role is for you if you are

  • Builds high-scale infrastructure where correctness and latency are tested together.
  • Can reason across distributed ingestion, streaming, storage, and delivery instead of treating pipeline boundaries as another team's problem.
  • Works effectively on a small team where technical judgment, direct collaboration, and production ownership matter.

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EdgeOrigin is an equal-opportunity employer. Completing the fields below is entirely voluntary and is kept separate from the hiring decision - it will not help or hurt your application in any way.

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EdgeOrigin is proud to be an equal-opportunity employer. We celebrate difference and are committed to building an inclusive team where everyone can do their best work, regardless of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. If you need a reasonable accommodation at any point in the process, tell us at [email protected] and we'll make it happen.

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