Senior Analytics Engineer

Jellyfish

1d • $150,000 - $230,000

About The Position

Jellyfish is seeking a Senior Analytics Engineer to enhance its data platform. This role involves transforming raw data into a reliable foundation for analytics, product development, and customer insights. The engineer will focus on creating durable models, improving data quality, and ensuring consistent definitions of business concepts across the organization. The ideal candidate values clean semantic models, reproducible transformations, and user-friendly data access.

Requirements

• Extreme comfort with complex SQL, including reasoning about performance, correctness, and maintainability.
• Experience with tools like dbt or similar transformation frameworks.
• Understanding of concepts such as staging models, intermediate models, marts, testing, lineage, and semantic layers.
• Strong data modeling fundamentals, including dimensional modeling, normalized and denormalized models, facts and dimensions, grain, and slowly changing dimensions.
• Understanding of how modeling decisions impact downstream consumers.
• A data quality mindset, viewing tests, contracts, and documentation as integral parts of the product.
• Ability to collaborate with engineers, analysts, product managers, and domain experts to translate business concepts into precise data definitions.
• Pragmatic problem-solving skills focused on delivering trustworthy, usable data.

Nice To Haves

• Experience in a rapidly scaling SaaS environment.
• Experience introducing dbt or an equivalent modeling framework into an existing data platform.
• Experience with Databricks, Delta Lake, or lakehouse architectures.
• Experience with data catalogs, lineage, or governance platforms like OpenMetadata.
• Experience defining semantic models or metric contracts consumed by both analytics and production applications.

Responsibilities

• Design and maintain analytical data models, transforming raw engineering and product data into understandable, reusable datasets.
• Define facts, dimensions, metrics, and canonical business entities for organizational sharing.
• Introduce and mature tools like dbt for managing transformations, testing, documentation, and lineage.
• Establish patterns for analytical transformations to improve understandability, review, and maintenance.
• Build automated checks for data quality, including completeness, freshness, uniqueness, and referential integrity.
• Detect data problems closer to their source rather than downstream.
• Partner with Product, Engineering, and Analytics to define key metrics and ensure consistent implementation across various platforms.
• Enhance data accessibility for engineers and analysts through documentation, examples, and reusable models.
• Facilitate understanding of data flow through the platform.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed