Senior Data Engineer II

About the role

The Senior Data Engineer at Jellyvision is responsible for building and operating data pipelines across both legacy and new platform infrastructure. They contribute to the data systems that support these pipelines and help improve the operational health of the stack as the platform evolves.

Responsibilities

• Build and operate data pipelines
• Design and build pipelines that support data movement across systems - ingestion, transformation, compliance, and cross-domain data flows
• Own pipeline operations end to end: monitoring, incident resolution, and data quality across both new and inherited workloads
• Make sound pipeline design decisions independently while working within the architecture the team has established
• Assess and improve existing data systems
• Develop working knowledge of how existing infrastructure fits together by mapping data flows, dependencies and performance characteristics
• Improve documentation, observability, data quality, and operational standards across the systems you work in
• Identify technical debt and reliability risks and bring recommendations grounded in meaningful impact
• Provision and manage the infrastructure your data workloads require, using established IaC practices and team standards
• Maintain and improve the reliability, performance, and cost-efficiency of the infrastructure you own
• Build and contribute to shared infrastructure as the platform evolves, building on new foundations as they become available
• Contribute across the data platform

Requirements

• 6–8+ years of data engineering experience with hands-on ownership of production systems
• Strong pipeline design and orchestration skills in a production environment
• Advanced SQL: complex transformations, performance tuning, and debugging against a cloud data lake or warehouse
• Strong Python: production-grade code, scripting, testing, and debugging
• Familiarity with infrastructure-as-code for provisioning and managing cloud data resources
• Experience inheriting and improving systems you didn't build - developing working knowledge, identifying risks, and making them better
• Clear written communication: you can document a system, a process, or a recommendation so others can act on it independently
• Comfortable working across team boundaries with engineering and product on data needs
• Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) to accelerate engineering workflows

Skills

• Data science or analytics background — comfortable with model inputs and outputs, statistical concepts, and supporting analytical or decision-support workflows
• Experience with dbt or comparable transformation frameworks: reading models, understanding grain and dependencies, writing tests
• Experience with managed ELT tools (Fivetran, Stitch, or similar)
• Experience in a regulated industry (healthcare, insurance, financial services) with familiarity around compliance-driven data requirements
• SaaS platform experience, particularly with multi-tenant data architectures