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# Data Engineer

- **Company:** Jump, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $190,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Data Systems, Python (Programming Language), Raw Data, Software Engineering, SQL Databases, Data Streaming, Snowflake, Git, Real Time Data, Machine Learning Operations, Tools for Reporting, Software Version Control, Data Pipelines
- **Published:** August 29, 2026
- **Apply:** https://jobs.ashbyhq.com/jump/fdd3464b-5508-4a92-b4fa-88af506f7cb4

## About the Role

* 6+ years of data engineering experience
 * Ability to independently own complex data systems from design through production and make sound architectural tradeoffs
 * Strong product and stakeholder instincts - you can translate ambiguous business needs into durable data solutions
 * Deep, production-grade SQL and Snowflake expertise
 * Deep experience with dbt, including testing, documentation, dependencies, and deployment
 * Software engineering discipline - Git-based version control, CI/CD for data pipelines, and strong Python skills for building data pipelines, orchestration, and custom transformations
 * A track record of building testing, monitoring, and alerting into pipelines before something breaks, not after

Nice to Haves

 * Familiarity with BI/analytics tools, ideally Sigma
 * Experience with orchestration and workflow tooling
 * Depth with cloud platforms
 * Exposure to supporting ML/AI pipelines or streaming and real-time data, * A strong desire to learn. You are curious and willing to dive deep to become a subject matter expert in our domain
 * Tenacity. You enjoy working on challenges that others can't or don't want to tackle and you aren't afraid of failing fast in order to find better solutions.
 * Passion. You love using your skills to solve real problems. You hold yourself to a high standard and help to elevate others as well.
 * Empathy. You thrive in an environment where everyone can truly be themselves. You understand that our differing life experiences influence who we are and how we show up, and these diverse perspectives enrich both our team and our product.
 * Customer-centric mindset. You put customers at the heart of everything they do, striving to understand their needs and exceed their expectations
 * Innovation: Passion for exploring and implementing AI technologies to enhance automation, optimize workflows, and drive innovation

## Description

Jump is seeking a Senior Data Engineer to join our nimble Data team, where you'll help build and evolve the data platform that powers the company. You'll own the reliability and performance of our Snowflake and dbt-powered stack, building the pipelines and models that make data trustworthy and usable across Jump - from internal dashboards to client-facing products. As we create and iterate on agentic functionality across our product, you'll help shape how Jump builds on and leverages its data foundation. This is a hands-on, high-ownership role spanning data platform, analytics engineering, and data infrastructure. You'll work across the stack, solving problems end-to-end while helping shape the architecture and data practices that will support Jump as we scale.

What You'll Do

 * Build and maintain ELT pipelines that turn raw data into modular, well-tested dbt models inside Snowflake
 * Design and maintain reliable data orchestration and workflows as the platform grows in complexity
 * Write and maintain dbt tests that catch issues before they reach a critical point
 * Document models and sources so the meaning of our data is discoverable
 * Build monitoring and alerting that surfaces pipeline failures and data anomalies before anyone has to ask
 * Optimize Snowflake table design and materialization strategy to keep performance high and costs in check as data volume grows
 * Help evolve Snowflake access controls and governance, keeping roles, grants, and security aligned with organizational standards
 * Dig into what internal teams and external clients actually need from our data, not just what they ask for
 * Bring a strong engineering discipline to data work; version control, CI/CD, code review
 * Build the data infrastructure and governance needed to support new AI/ML use cases, including reliable access to high-quality, well-understood data


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