Senior Analytics Engineer, Philo

• 8+ years of professional experience in analytics engineering, data modeling, or a closely related role, with significant hands-on experience building and maintaining production dbt projects or a similar data transformation tool
• Deep expertise in SQL and dbt, including advanced model design patterns (dimensional modeling, incremental models, slowly changing dimensions, and snapshot strategies)
• Experience developing in a BI tool that leverages semantic layers, such as Mode, Looker, Omni, SSAS Cubes, or similar
• Strong experience with Snowflake or a comparable cloud data warehouse, including performance tuning and cost optimization
• Proven ability to translate ambiguous business questions into clean, well-structured data models that stakeholders trust and rely on
• Experience defining, governing, and maintaining business metrics and KPIs across an organization
• Strong communication skills — you can explain data modeling decisions to a VP of Finance just as clearly as to a fellow engineer
• A strong sense of ownership and quality: you care deeply about data accuracy, documentation, testing, and making the data stack better over time
• Experience working with data quality and observability tools (e.g., BigEye, Elementary, dbt tests, Great Expectations, or similar)

Desirable

• Philo's Data Engineering team encompasses both data engineering and analytics engineering
• As an analytics engineer at Philo, you'll own the data modeling layer that turns raw event and application data into the clean, trusted data products the entire company relies on — from metric definitions and dimensional models to the governed semantic layer that ensures everyone is looking at the same numbers
• You'll work closely with data scientists, analysts, and business stakeholders across the organization to understand their analytical needs and translate them into well-modeled, well-documented, and performant data products in dbt and Snowflake
• We are passionate about providing a high-quality, trustworthy data platform for the entire company, using both cutting-edge technologies and proven engineering practices in close collaboration with every department
• Our analytics engineering stack centers on dbt and Snowflake, supported by tools like Segment, Avo, and BigEye for event tracking, data quality, and observability
• Some of the recent projects that analytics engineers at Philo have worked on include building new models for key financial data, dbt model design and refactoring, metric definition and governance, per-query cost optimization in Snowflake, and developing data quality testing and documentation frameworks
• We're looking for a Senior Analytics Engineer to own and elevate the data modeling layer that powers decision-making across Philo
• In this role, you'll be the person who transforms raw, messy application and event data into clean, trusted, well-documented data products that the entire company relies on
• You'll design and maintain dimensional models, define and govern key business metrics, and ensure semantic consistency across our data stack — so that when someone asks "what does this number mean?", there's always a clear, reliable answer
• This is a high-impact IC role: you'll partner directly with stakeholders across Finance, Product, Marketing, Ad Sales, and Engineering to deeply understand their analytical needs and translate them into scalable, performant dbt models and data products
• You'll also work closely with the data engineering side of the team to ensure the underlying infrastructure supports your modeling work, and with analysts and data scientists to make sure the data you deliver is easy to use, well-tested, and trustworthy
• The ideal candidate has strong opinions about how analytics engineering should be done — how to structure a dbt project, when to denormalize, how to manage slowly changing dimensions, how to build a metric layer that people actually trust — and the experience to back those opinions up
• Design, build, and maintain Philo's core dimensional models and data marts in dbt, serving as an owner of the analytics engineering layer of our data stack
• Define and govern key business metrics and KPIs, ensuring consistent definitions and reliable calculations across all downstream reporting and analytics
• Partner with stakeholders across the organization (Finance, Product, Marketing, Ad Sales, Content, and others) to understand their data needs and translate business questions into well-modeled, performant data products
• Establish and enforce best practices for dbt project structure, model design patterns, documentation, and testing — raising the quality bar for analytics engineering at Philo
• Build and maintain comprehensive data documentation and a data catalog so that analysts, data scientists, and business users can confidently self-serve
• Implement and maintain data quality checks and monitoring (e.g., via dbt tests, BigEye, or similar tools) to proactively catch issues before they reach stakeholders
• Optimize query performance and warehouse cost efficiency in Snowflake through thoughtful model design, materialization strategies, and incremental processing patterns
• Collaborate with data engineers on pipeline design and data ingestion to ensure raw data lands in forms that support clean, efficient downstream modeling
• Drive alignment across teams on data definitions, naming conventions, and a single source of truth — building stakeholder trust in data and reporting over time
• Contribute to the evolution of Philo's overall data platform strategy, particularly as it relates to the semantic/metric layer, data governance, and analytics engineering tooling