Senior Data Engineer
About The Position
Jellyfish is the backbone for elite engineering organizations, and our data pipelines need to be as high-performing and reliable as the teams we serve. We are looking for a Senior Data Engineer to help us build, automate, and execute the next generation of our Jellyfish data platform. Working closely with our Lead Data Architect, you’ll be responsible for implementing core data models, building production-grade CI/CD for data pipelines, and transforming raw engineering signals into highly optimized analytical layers. If you view broken pipelines and manual data patches as a technical debt to be solved and want to write code that directly impacts how the world’s best engineering leaders measure their output, you’re the perfect fit.
Requirements
- Solid, production-level experience with Python, advanced SQL, and data transformation frameworks (like dbt or PySpark).
- Highly comfortable working with programmatic orchestrators (such as Prefect, Dagster, or Airflow).
- Experience with enterprise data platforms (e.g., Snowflake, Databricks, BigQuery).
- Understand how to safely navigate environment boundaries, manage access keys securely, and write performant queries that don't balloon the cloud bill.
- An automation mindset: looking at repeated data backfill, a manual schema fix, or an untracked data quality bug and immediately thinking about how to script a permanent, automated solution.
- Collaborative builder: love working in a team, write readable code, value thorough documentation and clear data lineage, and enjoy collaborating with application engineers to solve complex data delivery problems.
- Pragmatic problem solver: know when to write a perfectly optimized distributed processing job and when a simple, well-indexed database table or cached view is the smartest move to keep the business moving.
- Humility, a performance-driven attitude, and a team-player approach.
- A sense of humor is a must.
- Applicants must be authorized to work for any employer in the US.
- Unable to sponsor or take over sponsorship of an employment visa at this time.
Nice To Haves
- Experience in a rapidly scaling startup handling complex, multi-tenant B2B SaaS data.
- Strong opinions on data quality testing frameworks (like Great Expectations or Soda) and data-observability patterns.
- Extensive experience with cloud cost allocation or tracked token-level spend for LLM/AI model integrations.
Responsibilities
- Maintain end-to-end data pipelines, writing clean, modular Python and SQL.
- Translate the architectural blueprint into reality, structuring data across our Medallion layers (Bronze > Silver > Gold) for maximum performance and reliability.
- Take the lead on migrating, optimizing, and maintaining workflow orchestration engines.
- Eliminate pipeline bottlenecks, leverage modern fast-paths (like Pydantic v2 and async database clients), and ensure distributed tasks scale seamlessly without hitting API limits.
- Build the "paved road" for data deployments.
- Use Terraform to provision data resources and write automated tests to validate schemas and data quality before code ever hits our isolated staging or production catalogs.
- Collaborate with product developers to expose data safely.
- Help design and optimize the application backend tiers, backend-for-frontend (BFF) layers, and Redis caching structures that protect our core data warehouse from frontend concurrency spikes.
- Participate in the data platform's incident response rotation.
- Build deep observability, refine alerts to reduce noise, and write programmatic fixes to ensure issues never happen again.
Benefits
- Occasional travel may be required.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
