Senior Analytics Engineer

About the Job

At Life360, we collect a lot of data: 60 billion unique location points , 12 billion user actions , 8 billion miles driven every single month , and so much more. As a Senior Analytics Engineer, you will be responsible for transforming this wealth of data into trusted, well-modeled datasets that power analytics, reporting, and data science initiatives across the organization. You should have a strong foundation in data modeling, SQL , and Python , deep understanding of business metrics, and a passion for making data accessible and understandable to stakeholders at all levels. Beyond modeling and analytics engineering, you will take on some data engineering responsibilities, working directly within our Databricks-based platform to help build and maintain the pipelines that feed the datasets you model.

For candidates based in the US, the salary range for this position is $148,000 to $218,500 USD. For candidates based out of Canada, the salary range for this position is 171,500 to $201,000 CAD. We take into consideration an individual's background and experience in determining final salary; therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

AI-Native Expectations

The Analytics Data Engineering team leverages LLMs to support code generation, analysis, and other use cases. Your experience with AI / LLM usage should include managing code generation with a close eye on quality, standards, and testing, while owning the outputs as your own. Your work with and ability to leverage these tools ( Claude, Cursor, CoPilot ) will drive your velocity and ability to effectively work within our environment.

What You’ll Do

Primary responsibilities include, but are not limited to:

• Design and implement robust dimensional and relational data models that support analytical use cases across Product, Marketing, Operations, and Finance
• Build and maintain scalable dbt transformation pipelines , ensuring high data quality, performance, and cost-efficiency from raw ingestion to business-ready outputs
• Own the transformation and modeling of curated (Silver/Gold) datasets, ensuring clear contracts and traceability from raw to business-ready data.
• Partner with data engineering to build and maintain data pipelines and Delta Lake tables within Databricks , including basic ingestion, transformation, and orchestration work.
• Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products that support experimentation, A/B testing, and advanced analytics
• Implement data quality tests, monitoring, SLAs, and alerting to ensure reliability of critical analytical datasets
• Enhance our LLM development support capabilities – creating tools / skills / agents that give our LLMs more context and help us continually improve their abilities to debug, create code, and maintain systems.
• Partner with Data Engineers to define and enforce data contracts, ensuring schema stability and minimizing downstream breakage
• Establish and evangelize analytics engineering best practices, including version control, code review, testing standards, and documentation
• Empower self-service analytics by building intuitive, well-documented data marts and semantic layers

Success in Year One

Within the first year in this role, the person hired will have built canonical gold and silver models for the reporting revamp and consolidation effort, serving as the source of truth across all business lines and products and reconciling cleanly against partner data. Building on that foundation, they will have automated revenue and performance reporting directly from these models, removing the manual overhead that currently sits between raw data and trusted numbers. They will have also delivered a semantic layer, enabling executives, Product, Finance, and Accounting to self-serve their own reporting and data requests without routing every question through the data team. Finally, they will have set up alerting for metric trend changes and anomalies, so issues surface proactively rather than being discovered after the fact.

What We’re Looking For

• Minimum 5+ years of experience in analytics engineering, data modeling, or similar roles working with enterprise-scale data, and demonstrated ownership of data products and cross-functional collaboration
• Experience using AI/LLM coding assistants (for example, GitHub Copilot, Cursor, or Claude Code ), with a disciplined approach to reviewing, testing, and owning generated code rather than accepting it at face value.
• Expert-level SQL skills with deep understanding of query optimization and performance tuning
• Extensive experience with dbt (data build tool) including testing, documentation, and package management
• Strong programming skills in Python for data manipulation, automation, and custom analytics workflows
• Strong understanding of dimensional modeling, star schemas, one big table, and other data modeling methodologies
• Working knowledge of the Databricks platform , including SQL Warehouses, Delta Lake, and Unity Catalog; experience with other modern cloud data warehouses ( Snowflake, BigQuery, or Redshift ) is a plus and readily transferable.
• Familiarity with orchestration frameworks and how analytics transformations are scheduled within broader data workflows 
• Experience working with version control systems ( Git ) and implementing CI/CD for analytics code
• Strong business acumen and ability to translate business requirements into well-designed data models
• Prior experience working with advertising, ad tech, or media data is preferred.
• Understanding of data governance, privacy, and compliance requirements ( GDPR, CCPA, and SOX ) 
• Familiarity with BI tools ( Tableau, Looker, Mode, or similar ) and how analysts consume data
• Strong ownership mindset with a passion for deeply understanding ambiguous business problems and translating them into clean, maintainable, and well-tested data models.
• Excellent communication skills with ability to explain technical concepts to both technical and non-technical audiences
• Dedication to data quality, documentation, and empowering others through self-service analytics
• Bachelor's degree or equivalent experience in Computer Science, Information Security, or a related field