Analytics Engineer

Job Description:

Providing for loved ones, planning rewarding retirements, saving enough for whatever lies ahead – our policyholders count on us to be there when it matters most. It’s a big ask, but it’s one that we have the power to deliver when we work together. We collaborate and innovate – pushing one another to transform not just Pacific Life, but the entire industry for the better.

Why? Because it’s the right thing to do. Pacific Life is more than a job; it’s a career with purpose. It’s a career where you have the support, balance, and resources to make a positive impact on the future – including your own.

We’re actively seeking a talented Analytics Engineer to join our Analytics Engineering team as part of our Advanced Analytics function in our Newport Beach, CA office. Reporting to the Director, Analytics Engineering, this role will help transform enterprise data into trusted, reusable analytics products that support reporting, machine learning, data science, and AI-powered business solutions. You’ll collaborate with business stakeholders, analytics delivery, data engineers, visualization engineers, and data scientists to translate business needs into tested, documented, and scalable analytical models.

Our work goes beyond building data transformations. We capture the business definitions, rules, and relationships that make data meaningful and usable by both people and AI agents. Working with dbt, Snowflake, Snowflake Cortex, AWS, and modern engineering practices, you’ll help deliver reliable analytics solutions while enabling stakeholders to confidently use data and AI. You’ll also contribute to our adoption of AI-assisted and agentic development using tools such as Claude Code and Git Hub Copilot, with appropriate testing, human review, and security controls.

How you will help move us forward
· Analytics Engineering:
Develop and maintain modular SQL transformations and analytical data models using dbt and Snowflake, creating reusable data sets for reporting, analysis, data science, and AI use cases.
· Business Context and Semantic Modeling:
Partner with stakeholders to understand business processes and translate requirements into clear data definitions, business rules, metrics, and semantic models. Document relationships and assumptions so data can be interpreted consistently.
· Data Quality and Trust:
Build automated tests, validate business logic, investigate discrepancies, and maintain documentation and lineage to help ensure analytical data is accurate, understandable, and reliable.
· AI and Agentic Solutions:
Contribute to the development, testing, and evaluation of Snowflake Cortex solutions and AI agents grounded in trusted enterprise data and business context.
· Modern Development Practices:
Use Git, Azure Dev Ops (ADO), and CI/CD workflows to manage work, collaborate through pull requests, automate validation, and deliver controlled changes across development and production environments.
· AI-Assisted and Agentic Development:
Apply approved tools such as Claude Code and Git Hub Copilot to development, testing, documentation, and workflow automation. Review and validate AI-generated outputs while following engineering, privacy, and security standards.
· Stakeholder Enablement:
Provide hands-on guidance, demonstrations, documentation, and knowledge transfer that help business partners adopt analytics products, use Snowflake Cortex, and become more self-sufficient.
· Reliability and Continuous Improvement:
Troubleshoot analytical models and workflows, improve query performance and efficiency, modernize manual processes, and contribute reusable patterns and improvements to the team’s engineering playbook.
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Cross-Functional Collaboration:

Work with data engineering, platform,…