Senior Analytics Engineer

Let’s talk about what you’ll do
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.

Let’s talk about what you need to excel
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.

Bonus points if you have 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