Senior Analytics Engineer

PandaDoc is a company that empowers organizations by automating document workflows. They are seeking a Senior Analytics Engineer to join their data team, where the main responsibilities include building foundational data models and infrastructure to support analytics, partnering with business stakeholders, and ensuring data quality and reliability for strategic decision-making.

Responsibilities:

• Design, build, and maintain dimensional data models in Snowflake that serve as the foundation for analytics and reporting across the company
• Develop and optimize dbt models to transform raw data from systems like Salesforce, HubSpot, Recurly, and other business platforms into clean, reliable datasets
• Create and maintain data documentation in Select Star and other catalog tools to ensure discoverability and understanding of our data assets
• Partner with data analysts and business teams to understand their analytical needs and translate them into scalable data solutions
• Implement data quality checks and monitoring to ensure accuracy and reliability of analytics datasets
• Optimize SQL queries and data pipelines for performance and cost efficiency
• Support strategic analytics initiatives including customer journey analysis, revenue analytics, and product usage metrics
• Contribute to data governance practices including data quality standards, PII handling, and metadata management
• Mentor junior team members and promote best practices in data modeling and analytics engineering

Requirements:

• 5+ years of experience in analytics engineering, data engineering, or similar data-focused role
• Expert-level SQL skills with experience writing complex queries, CTEs, and window functions
• Strong experience with dbt (data build tool) for building and maintaining transformation pipelines
• Hands-on experience with Snowflake or other cloud data warehouses (e.g. BigQuery, Redshift)
• Familiarity with data cataloging tools (Select Star preferred)
• Knowledge of data orchestration tools (Airflow / MWAA preferred)
• Strong background in Python for data analysis or automation
• Deep understanding of dimensional modeling, data warehouse design patterns, and analytics best practices
• Experience working with SaaS metrics (MRR, churn, customer lifetime value, etc.)
• Proficiency with GitHub for version control and collaborative development
• Strong communication skills with ability to translate technical concepts for business stakeholders
• Self-directed and comfortable working in a remote environment with distributed teams
• Experience with reverse ETL tools such as Hightouch
• Some exposure to BI tools (Hex preferred)
• Understanding of data mesh or domain-oriented data architecture concepts