Principle 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 a Principle Analytics Engineer, you’ll play a crucial role in bridging the gap between data analysis and engineering. Your focus will be on creating robust data models, designing efficient pipelines, and enabling end-users to extract meaningful insights from complex datasets. If you’re passionate about data-driven decision-making and enjoy combining technical expertise with business acumen, this role might be a perfect fit for you.


How you’ll help move us forward:

Thought Leadership: 

• Stay engaged with cross industry data and analytics engineering thought leadership, synthesize key concepts, and drive practical adoption within the department 

Upskilling and Collaboration: 

• Act as a mentor to other engineers, promoting a culture of continuous learning and improvement. 
• Lead workshops and training sessions on dbt, Snowflake, and other data engineering best practices. 
• Foster a collaborative environment where knowledge sharing is encouraged. 

Documentation and Communication: 

• Maintain clear and comprehensive documentation for data models, pipelines, and feature engineering processes. 
• Communicate results, insights, and findings to stakeholders, including technical and non-technical audiences. 
• Collaborate with cross-functional teams to align analytics efforts with business goals. 

Data Modeling: 

• Design and develop modular data models using dbt Cloud that accurately represent business entities, relationships, and hierarchies. 
• Optimize data structures for efficient querying and reporting. 
• Collaborate with data scientists and analysts to understand their requirements and translate them into actionable data models. 

Data Pipeline Development: 

• Build and maintain data pipelines to transform and process raw data from various sources (databases, APIs, logs, etc.). 
• Ensure data quality, consistency, and reliability throughout the pipeline. 
• Implement ELT processes using dbt and Snowflake to prepare data for analysis. 

Performance Optimization: 

• Optimize data processing performance, considering factors like scalability, latency, and resource utilization. 
• Monitor and troubleshoot data pipelines to ensure smooth operation. 
• Identify bottlenecks and propose improvements. 

The experience you bring:

• Bachelor’s degree in Computer Science, Data Science, or related fields. 
• 10+ years of experience as an Analytics Engineer, Data Engineer, or Data Scientist in a company with large, complex data sources. 
• Expertise in SQL, dbt, Python, and other programming languages for data manipulation. 
• Experience with data modeling, ETL/ELT processes, and database design. 
• Strong analytical and problem-solving skills. 
• Excellent communication and collaboration abilities. 
• Experience providing technical leadership and mentoring other engineers for best practices on analytics engineering. 
• Expertise with AWS platform, services, and tools. 
• Expertise in Snowflake. 
• Experience with unstructured and streaming data. 
• Familiarity with agile practices and tools (e.g., dbt, ADO) 

​What makes