Why This Job is Featured on The SaaS Jobs
This Data Engineer role stands out in SaaS because it sits on the data platform layer that underpins a product used by engineering leaders. The work described spans classic warehouse modeling through a Medallion approach, orchestration with tools like Prefect or Dagster, and the operational realities of keeping customer-facing analytics dependable. In a subscription software context, that combination tends to be tightly coupled to product trust, reporting accuracy, and the ability to ship insights without manual intervention.
From a career perspective, the remit builds durable SaaS data platform skills: production-grade Python and SQL, workflow reliability, and infrastructure-as-code ownership with Terraform across staged environments. The emphasis on observability and an on-call rotation signals experience with incident response and measurable platform health, which translates well across modern SaaS teams. Collaboration with application engineers on API and caching patterns also broadens exposure beyond batch pipelines into how data is safely served to product surfaces.
This role is best suited to someone who prefers autonomous build-and-own work, values clean abstractions and repeatable automation, and is comfortable being accountable for operational outcomes. It will appeal to engineers who want to deepen platform engineering craft while staying close to how SaaS products expose and protect data at scale.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 Data Engineer to join our data platform team and help us execute, automate, and maintain the next generation of our Jellyfish data platform. In this role, you’ll be a core builder—fully autonomous, highly proficient, and responsible for translating architectural blueprints into clean, production-grade pipelines. If you view manual data patches and unmonitored workflows as bugs to be squashed and want to write code that directly impacts how the world’s best engineering leaders measure their output, you’re the perfect fit.
What you’ll actually be doing:
Core Pipeline Engineering – You’ll write the clean, modular Python and optimized SQL that drives our daily data transformations. You will be responsible for implementing our Medallion-layer data models (Bronze → Silver → Gold), ensuring high performance and data integrity.
Modern Orchestration & Tuning – You’ll manage and tune our workflow orchestration engines (like Prefect or Dagster). You’ll hunt down slow execution paths, optimize parameter serialization (e.g., leveraging Pydantic v2), and ensure our distributed processing jobs run efficiently.
Infrastructure as Code (IaC) – You won't just write data scripts; you'll own your infrastructure deployment. You will use Terraform to manage and provision data warehouse schemas, permissions, and tables across securely isolated staging and production catalogs.
API & Caching Integration – You’ll collaborate with product developers to expose data safely. You’ll help implement and maintain the application backend tiers, backend-for-frontend (BFF) layers, and Redis caching structures that protect our core data warehouse from frontend concurrency spikes.
On-Call & Pipeline Observability – You’ll participate in our data platform's incident response rotation. When a pipeline breaks, you won't just fix the data; you’ll refine the Datadog dashboards and alerts to ensure we catch the issue earlier next time.
You’re a great fit if:
Data Engineering Fluency – You have solid, hands-on production experience with Python, advanced SQL, and data transformation concepts. You are comfortable building and scheduling workflows using programmatic orchestrators (such as Prefect, Dagster, or Airflow).
Warehouse & Catalog Practitioner – You know your way around enterprise data platforms (e.g., Snowflake, Databricks, BigQuery). You understand how to navigate environment boundaries, manage access keys securely, and write performant queries.
Automation Mindset – You look at a repeated data backfill, a manual schema fix, or an untracked data quality bug and immediately think about how to script a permanent, automated solution.
Collaborative Builder – You love working in a team. You 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 – You 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.
Bonus Points:
You’ve worked in a rapidly scaling startup handling complex, multi-tenant B2B SaaS data.
You have experience with data quality testing frameworks (like Great Expectations or Soda).
You’ve interacted with cloud cost allocation tracking or token-level spend for LLM/AI model integrations.
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 enables leaders to effectively build AI-integrated engineering teams, align engineering decisions with business initiatives and deliver the right software efficiently and on time. AI tools alone won’t transform your org—Jellyfish shows you what’s working, what’s not, and how to build high-performing teams that know how to use AI the right way.
