Note: The job is a remote job and is open to candidates in USA. Jellyfish is an intelligence platform for AI-integrated engineering that helps organizations understand and improve how they build software. The company is seeking a Senior Analytics Engineer to develop trusted, reusable data models, improve data quality, and establish consistent business metrics across analytics, product, and customer-facing experiences.
Responsibilities
- 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
Skills
- 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
- 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
- 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
Benefits
- Offers Equity
- Remote work arrangement
Company Overview