Jellyfish helps engineering organizations understand how their teams work, and that starts with data people can actually trust. We are looking for a Senior Analytics Engineer to help turn our growing data platform into a consistent, well-modeled foundation for analytics, product development, and customer-facing insights. You’ll sit between raw data and the people consuming it, defining durable models, improving data quality, and making sure important business concepts mean the same thing everywhere they appear.
If you care about clean semantic models, reproducible transformations, and making it easy for others to confidently use data, you’re the perfect fit.
What you’ll actually be doing:
Data Modeling - You’ll design and maintain analytical data models that turn raw engineering and product data into understandable, reusable datasets. You’ll help define facts, dimensions, metrics, and canonical business entities that can be shared across the organization.
Transformation Frameworks - You’ll help introduce and mature tools like dbt for managing transformations, testing, documentation, and lineage. You’ll establish patterns that make analytical transformations easier to understand, review, and maintain.
Data Quality - You’ll build automated checks for completeness, freshness, uniqueness, referential integrity, and other important quality signals. You’ll help move us from discovering bad data downstream to detecting problems closer to their source.
Metric Consistency - You’ll partner with Product, Engineering, and Analytics to establish clear definitions for important metrics and ensure those definitions are implemented consistently across dashboards, APIs, and customer-facing experiences.
Developer Enablement - You’ll make it easier for engineers and analysts to understand and use our data. That includes documentation, examples, reusable models, and helping teams understand how data flows through the platform.
You’re a great fit if:
SQL Fluency - You are extremely comfortable working with complex SQL and can reason about performance, correctness, and maintainability.
Analytics Engineering Experience - You’ve worked with tools like dbt or similar transformation frameworks and understand concepts like staging models, intermediate models, marts, testing, lineage, and semantic layers.
Strong Data Modeling Fundamentals - You understand dimensional modeling, normalized and denormalized models, facts and dimensions, grain, slowly changing dimensions, and how modeling decisions affect downstream consumers.
Data Quality Mindset - You think of tests, contracts, and documentation as part of the product, not cleanup work.
Collaborative Translator - You can work with engineers, analysts, product managers, and domain experts to turn ambiguous business concepts into precise data definitions.
Pragmatic Problem Solver - You understand that the goal is trustworthy, usable data, not building the theoretically perfect warehouse.
Bonus Points:
You’ve worked in a rapidly scaling SaaS environment.
You’ve helped introduce dbt or an equivalent modeling framework into an existing data platform.
You’ve worked with Databricks, Delta Lake, or lakehouse architectures.
You’ve worked with data catalogs, lineage, or governance platforms like OpenMetadata.
You’ve helped define semantic models or metric contracts consumed by both analytics and production applications.
A list of job experiences and qualification requirements is great, but humility, a performance-driven attitude, and a team-player approach are most important to us. We love to have fun and win in the process. We only hire people who have a passion for building great companies in an environment where a sense of humor is a must.
Occasional travel may be required.
Applicants must be authorized to work for any employer in the US. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Let’s talk about us!
This is all about you, but you want to know a little about us. Jellyfish is the leading intelligence platform for AI-Integrated engineering, helping more than 1,000 companies including DraftKings, Keller Williams and Blue Yonder, leverage AI to transform how they build software. By combining the industry’s deepest engineering dataset with context-rich intelligence, Jellyfish helps R&D organizations understand what’s driving impact, adopt proven industry best practices, and make smarter decisions across AI adoption, planning, delivery, and engineering performance.
