Senior Data Engineer
Data Engineer · Senior · Full Time · Remote
Opens Jump's application page
Role
What you'll do.
Jump is seeking a Senior Data Engineer to own the reliability and performance of a Snowflake and dbt-powered data platform that powers fan engagement operations across NBA, WNBA, and NWSL teams. You'll design ELT pipelines, implement comprehensive monitoring and governance, and build infrastructure supporting AI/ML initiatives while collaborating with cross-functional teams. This hands-on role requires 6+ years of production data engineering experience with deep expertise in SQL, Snowflake, dbt, Python, and software engineering best practices to scale Jump's data capabilities.
Responsibilities
- Build and Maintain ELT Pipelines: Design, develop, and maintain robust Extract-Load-Transform pipelines that convert raw data into modular, well-tested dbt models within Snowflake. Focus on data quality, maintainability, and adherence to software engineering best practices including version control and code review standards.
- Design Data Orchestration and Workflows: Engineer and maintain reliable data orchestration systems and workflows that scale with platform complexity. Implement sophisticated scheduling and dependency management to ensure pipelines execute reliably and handle failures gracefully as data volumes grow exponentially.
- Implement Comprehensive Testing Strategy: Write, maintain, and evolve dbt tests that proactively catch data quality issues before they reach critical production systems. Establish testing frameworks and assertions that prevent data anomalies from impacting internal dashboards and client-facing products.
- Create Data Documentation and Discoverability: Document data models, sources, lineage, and transformations using dbt documentation capabilities and metadata systems. Ensure data meaning and usage patterns are discoverable across the organization, enabling self-service analytics and reducing dependency on data engineers.
- Build Monitoring and Alerting Infrastructure: Design and implement comprehensive monitoring and alerting systems that surface pipeline failures and data anomalies proactively. Create runbooks and escalation procedures that enable rapid incident response and continuous platform reliability.
- Optimize Snowflake Performance and Costs: Architect and optimize Snowflake table design, materialization strategies, and resource utilization to maintain high performance while controlling cloud infrastructure costs. Implement query optimization, clustering strategies, and resource management as data volume and complexity scale.
- Evolve Data Governance and Access Control: Design and maintain Snowflake access control frameworks, role hierarchies, and permission grants aligned with organizational security standards. Implement governance policies that support compliance requirements while enabling efficient data access for authorized stakeholders.
- Translate Business Requirements into Data Solutions: Partner with internal teams and external clients to deeply understand their data needs beyond surface-level requirements. Translate ambiguous business objectives into durable, scalable data solutions that provide genuine business value and support product innovation.
- Apply Software Engineering Discipline to Data Work: Bring production-grade software engineering practices to data development including Git-based version control, CI/CD pipelines for data deployments, code reviews, and continuous integration testing. Establish and maintain data engineering standards that mirror application development practices.
- Support AI and ML Infrastructure Development: Build data infrastructure, feature engineering pipelines, and governance frameworks that enable reliable AI/ML use cases. Ensure models have access to high-quality, well-documented datasets with complete lineage and quality guarantees required for production machine learning systems.
Qualifications
What we look for.
Technical
Advanced SQL and Snowflake Expertise
Production-grade proficiency in complex SQL including window functions, CTEs, query optimization, and performance tuning. Deep hands-on experience with Snowflake architecture, clustering, cost optimization, role-based access control, and advanced features like streams and dynamic tables.
dbt Mastery
Extensive experience with dbt including model development, testing framework implementation, documentation generation, dependency management, packages, and deployment pipelines. Demonstrated expertise in applying dbt best practices and architectural patterns for scalable analytics engineering.
Python for Data Engineering
Strong Python programming skills for developing data pipelines, custom transformations, orchestration logic, and utility scripts. Proficiency with libraries including pandas, sqlalchemy, and standard data processing frameworks. Ability to write production-quality Python code with proper error handling.
Data Pipeline Architecture and Design
Proven ability to design end-to-end data pipelines from source systems through transformation and consumption layers. Experience implementing ELT patterns, data quality frameworks, error handling mechanisms, and scalable pipeline architectures supporting analytical workloads.
Git-Based Version Control and CI/CD
Expertise with Git workflows, branching strategies, and pull request processes. Experience building and maintaining CI/CD pipelines for data deployments, automated testing frameworks, and continuous integration systems that apply software engineering discipline to data work.
Data Testing and Quality Assurance
Demonstrated track record of implementing comprehensive data quality frameworks including schema validation, row count checks, anomaly detection, and business logic assertions. Proactive approach to catching data issues before production impact through preventive testing strategies.
Data Monitoring and Observability
Experience building monitoring systems, alerting frameworks, and observability solutions for data pipelines. Ability to implement SLOs for data freshness and quality, create dashboards for pipeline health, and establish runbooks for incident response.
Education
Bachelor's Degree in Computer Science, Engineering, or Related Field
Formal education providing foundation in computer science principles, data structures, algorithms, and software development fundamentals relevant to data engineering roles. Equivalent professional experience strongly considered.
Experience
6+ Years of Production Data Engineering
Extensive hands-on experience building and maintaining data platforms and pipelines in production environments. Proven track record of scaling data systems, managing large-scale data warehouses, and solving complex data engineering challenges at organizational scale.
Ownership of Complex Data Systems
Demonstrated ability to independently own and architect complex data systems from design phase through production operation. Experience making sound architectural tradeoffs, managing technical debt, and establishing patterns that support system scalability and reliability.
Cross-Functional Collaboration with Stakeholders
Experience working closely with data analysts, scientists, product managers, and business stakeholders. Strong track record of translating ambiguous business requirements into data solutions and building products that deliver measurable value to internal and external users.
Building Data Quality and Reliability Practices
Proven history of establishing testing, monitoring, and alerting infrastructure as foundational components rather than reactive additions. Experience preventing data incidents through comprehensive quality assurance and proactive observability in production systems.
Skills
Required
Snowflake
Production expertise with Snowflake data warehouse including SQL optimization, table design, clustering strategies, access control, cost management, and advanced features. Deep understanding of Snowflake architecture and best practices for analytical workloads.
dbt (Data Build Tool)
Advanced proficiency with dbt for analytics engineering including model development, testing frameworks, documentation, dependency management, and CI/CD integration. Experience with dbt best practices, macro development, and package management.
SQL
Expert-level SQL skills including complex queries, window functions, CTEs, query optimization, and performance tuning. Ability to write efficient, maintainable SQL code that scales with data volume and complexity.
Python
Strong Python programming for data engineering tasks including pipeline development, data transformations, orchestration logic, and scripting. Proficiency with data libraries and production-quality code practices.
Data Pipeline Development
Comprehensive experience designing and implementing ELT pipelines, data transformations, and orchestration workflows. Ability to architect end-to-end data flows that scale reliably with organizational data growth.
Git Version Control
Proficiency with Git workflows, branching strategies, pull requests, and collaborative code management. Ability to implement version control best practices for data code and configuration.
Data Quality and Testing
Experience implementing comprehensive data quality frameworks, anomaly detection, schema validation, and business logic testing. Proactive approach to preventing data issues in production systems.
Data Monitoring and Alerting
Experience building monitoring systems for data pipelines including alert mechanisms, SLO tracking, and incident response. Ability to implement observability for data infrastructure.
Preferred
Sigma Analytics
Nice to haveFamiliarity with Sigma or similar modern BI/analytics tools that integrate with cloud data warehouses. Experience building client-facing analytics and self-service BI solutions.
Data Orchestration Tools
Nice to haveExperience with Airflow, Dagster, dbt Cloud, or similar orchestration platforms. Expertise implementing workflow scheduling, dependency management, and failure handling at scale.
Cloud Platform Expertise
Nice to haveDeep experience with cloud platforms including AWS, GCP, or Azure. Understanding of cloud-native data architectures, scalability patterns, and cloud cost optimization strategies.
ML/AI Pipeline Support
Nice to haveExperience supporting machine learning and AI pipelines including feature engineering, model serving infrastructure, and data preparation for ML workloads. Understanding of ML-specific data quality and governance requirements.
Real-Time and Streaming Data
Nice to haveExposure to streaming data platforms, real-time processing frameworks, and event-driven architectures. Experience building systems that handle continuous data streams and millisecond-latency requirements.
Data Governance and Security
Nice to haveExperience implementing data governance frameworks, compliance controls, data lineage tracking, and security best practices in data systems.
Sports and Entertainment Industry Experience
Nice to haveBackground in sports technology, fan engagement platforms, or entertainment industry data systems. Understanding of unique data requirements in ticketing, merchandise, and game day operations.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 190,000 – 210,000
Equity·Stock options
Benefits
Remote-First Work Environment
Fully remote position with flexibility to work from anywhere. Jump operates as a distributed team with asynchronous communication and flexible schedules supporting work-life balance.
Comprehensive Health Coverage
Generous medical, dental, and vision insurance plans. Coverage extends to preventive care, specialist services, and family plans with competitive employee contribution rates.
Flexible Paid Time Off
Flexible PTO policy with no cap on vacation days. Encourages employees to take time off as needed for rest, travel, and personal pursuits while maintaining team commitments.
401(k) Retirement Plan
Company-sponsored 401(k) retirement savings plan with employer matching contributions. Supports long-term financial planning and wealth building.
Paid Parental Leave
16 weeks of fully paid parental leave available for both primary and secondary caregivers. Demonstrates commitment to supporting employees during major life transitions.
Work-From-Home Tech Setup Stipend
1,000 dollar reimbursement for work-from-home technology setup including ergonomic furniture, monitors, keyboards, webcams, and other equipment needed for productive remote work.
Professional Development Budget
1,000 dollar annual reimbursement for learning and development opportunities including courses, certifications, conferences, and training programs relevant to career growth.
Sustainability Initiative
Company-paid carbon offset subscriptions that neutralize employee travel and activities. Demonstrates environmental responsibility and supports sustainability values across the organization.
Competitive Equity Package
Generous stock options providing ownership stake and upside potential as Jump grows. Equity packages reflect role level and tenure with the company.
Full posting
Original listing.
The Company
Jump is transforming the live sports experience with the only end-to-end fan engagement platform built specifically for sports teams and venues. By focusing on aligned incentives between teams and fans, our platform unifies ticketing, merchandise, and game day operations - creating a smoother, more fan-friendly experience. Jump was founded by e-commerce innovator Marc Lore, MLB legend Alex Rodriguez, and entrepreneur Jordy Leiser. We're backed by top investors including Alexis Ohanian's Seven Seven Six and Forerunner Ventures.
Our platform powers teams across the NBA, WNBA, and NWSL among other leagues, helping them boost ticket sales and deliver innovative fan experiences. We're a remote-first team driven by core values - begin and build with trust, play like the underdog, win as a team, and do your thing. If you're collaborative, adaptable, and eager to shape the future of live sports, Jump is the place for you.
The Role
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
What We're Looking For
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
Attributes that will make you successful on our team
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
Benefits
Remote first
Competitive salary and equity
Flex PTO policy
401(k)
Generous medical, dental and vision plans
16 weeks paid parental leave for primary and secondary caregivers
$1,000 reimbursement for work-from-home tech setup
$1,000 reimbursement for learning and development
Company-paid sustainability subscription to ensure carbon neutrality is maintained for employee activities, such as travel
Compensation
Compensation is something we don't want our candidates or employees to worry about. Our goal is to offer competitive salaries that are regularly benchmarked against the market. The core tenants of our compensation philosophy are fairness and transparency. We have established a standardized leveling framework based on job scope and responsibilities. This means that every person at a certain level is paid the same as everyone else, regardless of their background, previous compensation, location, or any other factor. The compensation for this role is $190,000-$210,000 and includes a generous equity package.
Application
Some candidates may see the requirements and feel unsure that they match all the criteria. We encourage you to apply! There's a good chance you have important skills that we have not stated. We especially encourage members of traditionally underrepresented communities to apply, including women, nonbinary folx, people of color, members of the LGBTQ community, veterans, and people with disabilities. We're committed to building an inclusive workplace where everyone can bring their authentic self and thrive, and we value the diversity brought by different life experiences.
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