# Senior Data and AI Platform Engineer

**Company:** [Jellyfish](https://hotfix.jobs/companies/jellyfish)
**Location:** Remote
**Role:** Data Engineering
**Salary:** $150k – $230k/yr
**Experience:** 7+ years
**Skills:** Databricks, Unity Catalog, Delta Lake, SQL, Python, Lakehouse Architecture, Terraform, dbt, Ai Platforms, Data Governance, Data Lineage, Data Ingestion, Data Pipelines
**Posted:** 2026-06-29

> Senior individual contributor building and leading Jellyfish's Databricks Lakehouse platform to power enterprise analytics, BI, agentic AI workflows, and data science. Requires deep data platform engineering experience, strong Databricks expertise, Python/SQL skills, and ability to deliver governed, production-grade data foundations for AI.

## Job Description

## Responsibilities
- Design, build, and maintain data platforms and pipelines that support analytics, data science, BI and AI across Jellyfish.
- Lead the evolution of Jellyfish’s Databricks Lakehouse platform, helping define the architecture, governance model, development patterns, and operating practices that make the platform reliable, scalable, and easy to use.
- Enable enterprise-wide agentic analytics within Databricks with trusted datasets, semantic definitions, well-managed context, and well-governed agentic access.
- Mature core Databricks platform capabilities, including Unity Catalog, governed data access, data lineage, metadata management, compute patterns, environment management, and platform observability.
- Build and maintain ingestion pipelines that bring high-value data into the Lakehouse.
- Partner on data flows that send trusted data to other internal systems.
- Collaborate with data scientists, analysts, product managers, engineering leaders, customer success leaders, go-to-market leaders, and other stakeholders to understand analytical needs and design durable platform solutions.
- Create standards and reusable patterns for data modeling, documentation, observability, testing, governance, and AI-readiness across the data platform.
- Develop tools and processes to monitor data platform health, pipeline reliability, cost, performance, usage, and trust.
- Provide technical leadership as a senior individual contributor by setting architectural direction, raising engineering standards, mentoring teammates, and helping the team make high-quality technical decisions.
- Stay current with emerging Databricks and AI capabilities, evaluate where they can create real value for Jellyfish, and help turn promising ideas into production-ready platform capabilities.

## Requirements
- Deep experience in data engineering, data platform engineering, analytics engineering, or related roles.
- Designed, built, and operated reliable data platforms or large-scale data pipelines in production.
- Strong experience with Databricks or similar lakehouse/data platform technologies.
- Understand how to build governed, well-modeled data assets that can support BI, analytics, data science, and AI use cases.
- Experience with data ingestion, transformation, orchestration, testing, monitoring, and data quality practices.
- Advanced SQL skills and experience working with multiple database and warehouse technologies.
- Strong programmer with experience building production-grade systems in Python or similar languages.
- Understand the importance of metadata, documentation, lineage, access control, and semantic context in making data trustworthy and usable.
- Excited about AI and agentic analytics, but understand that successful AI depends on strong data foundations, clear definitions, governance, evaluation, and operational discipline.
- Comfortable working with technical and non-technical stakeholders, translating ambiguous needs into durable platform capabilities.
- Operate as a senior individual contributor: lead through architecture, judgment, communication, influence, and execution without needing to be a people manager.
- Strong communication skills and enjoy working as part of a cross-functional team.

## Nice-to-Haves
- Experience with Databricks Unity Catalog, Databricks SQL, Lakehouse architecture, Delta Lake, Databricks Workflows, Databricks Apps, Genie, or related Databricks AI/BI capabilities.
- Building platforms for self-service analytics, governed BI, semantic layers, metrics layers, or AI-assisted analytics.
- Designing data platforms that support LLMs, agents, retrieval-augmented generation, MCP, or other AI-enabled workflows.
- Infrastructure-as-code and platform automation tools such as Terraform, Databricks Asset Bundles, CI/CD pipelines, or similar technologies.
- dbt and modern analytics engineering practices.

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