Senior Data and AI Platform Engineer

Jellyfish is the leading intelligence platform for AI-Integrated engineering, helping more than 1,000 companies leverage AI to transform how they build software. As a Senior Data and AI Platform Engineer, you will support the mission to deliver product innovation and analytics by building and maintaining data platforms that power enterprise-wide analytics and AI-enabled workflows.

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
• Be a leader in enabling enterprise-wide agentic analytics within Databricks with trusted datasets, semantic definitions, well-managed context, and well-governed agentic access
• Help 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:

• You have deep experience in data engineering, data platform engineering, analytics engineering, or related roles
• You have designed, built, and operated reliable data platforms or large-scale data pipelines in production
• You have strong experience with Databricks or similar lakehouse/data platform technologies, and you are excited to help make Databricks a central platform for analytics and AI
• You understand how to build governed, well-modeled data assets that can support BI, analytics, data science, and AI use cases
• You have experience with data ingestion, transformation, orchestration, testing, monitoring, and data quality practices
• You have advanced SQL skills and experience working with multiple database and warehouse technologies
• You are a strong programmer, with experience building production-grade systems in Python or similar languages
• You understand the importance of metadata, documentation, lineage, access control, and semantic context in making data trustworthy and usable
• You are excited about AI and agentic analytics, but you also understand that successful AI depends on strong data foundations, clear definitions, governance, evaluation, and operational discipline
• You are comfortable working with technical and non-technical stakeholders, translating ambiguous needs into durable platform capabilities
• You operate as a senior individual contributor: you can lead through architecture, judgment, communication, influence, and execution without needing to be a people manager
• You love learning new things and teaching others what you know
• You have strong communication skills and enjoy working as part of a cross-functional team
• 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