About the role

Role overview

As a Data Engineer at Jellyfish (full remote), you’ll translate architecture into clean, production-grade data pipelines. You’ll build and tune orchestration workflows, deploy infrastructure, collaborate with product developers, and join an incident response rotation.

Key missions

Core Pipeline Engineering

Write modular, production-grade Python and optimized SQL for daily data transformations
Implement Medallion-layer data models

Modern Orchestration & Tuning

Manage and tune workflow orchestration engines
Optimize execution paths and ensure efficient distributed processing jobs

Infrastructure as Code (IaC)

Own data platform infrastructure deployment using Terraform
Manage warehouse schemas, permissions, and tables

What you’ll bring

Strong hands-on production experience with Python, advanced SQL, and data transformation concepts
Ability to build and schedule workflows using programmatic orchestrators such as Prefect, Dagster, or Airflow
Experience with enterprise warehouse/catalog platforms such as Snowflake, Databricks, or BigQuery
Pragmatic judgment: know when to optimize distributed jobs vs. use indexed tables/cached views
Automation mindset for turning manual fixes/backfills into permanent solutions
Collaborative engineering habits: readable code, thorough documentation, and clear data lineage
Experience working in rapidly scaling, multi-tenant B2B SaaS environments

Nice to have

Data quality testing frameworks such as Great Expectations or Soda
Experience with cloud cost allocation tracking or token-level spend tracking for LLM/AI integrations

About Jellyfish

Jellyfish is a company that provides data and engineering infrastructure for elite engineering organizations. It focuses on building and operating reliable data pipelines, orchestration, and data platform capabilities to help teams deliver and measure outcomes effectively.

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