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# Senior Data & AI Platform Engineer

- **Company:** Beazley Plc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Databases, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Migration, Relational Databases, Software Design Documents, Software Engineering, Large Language Models, Snowflake, Agentic-AI, Build Management, AI Platforms, Data Pipelines
- **Published:** October 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=48f3d4643f16a042

## About the Role

Senior engineering foundation

 * 6+ years of software engineering experience, including owning production systems from design through day-to-day operation.
 * AI-first in how you work. You use AI tools daily to design, build, test, and review code, and you own the quality of what ships.
 * You proactively work to spot problems before they are assigned, raise them early, and follow through to resolution.
 * You are eager to pick up new tools, domains, and codebases quickly, and you are comfortable asking questions.
 * Strong database, ORM, and dbt/ETL skills, and experience building on AWS and Azure.
 * Clear communicator in writing and in conversation, from design docs to code reviews.
 * Helps teammates grow through mentoring and thoughtful feedback.

Data & AI platform qualifications

 * Hands-on experience with Snowflake and relational databases at scale, and good judgment about when to use each.
 * Experience building data pipelines, both scheduled and event-driven.
 * Experience building AI or LLM-powered systems on company data, such as retrieval, embeddings, or agents.
 * Experience keeping sensitive client data secure at rest and in transit.
 * Experience with cybersecurity data, such as vulnerabilities, threat intelligence, or security telemetry.
 * Has led a data migration or consolidation from start to finish.

## Description

* Design and build a consolidated data platform across Snowflake, relational transaction-based databases, and other data sources to use for both internal and customer-facing applications.
 * Build reliable pipelines that move data from our security products to where it is needed.
 * Make data fast for our customer-facing products and trustworthy for reporting and analytics.
 * Build the foundation for AI: clean, well-organized data that AI agents and tools can use safely.
 * Set data standards and help other engineers adopt them.