Senior Analytics Engineer

1Password is looking for a Senior Analytics Engineer to join its Analytics & Data Engineering team. You'll work at the intersection of data engineering and analytics, partnering with analysts and stakeholders across Product, Finance, GTM, and Marketing to turn raw data into trusted, reusable datasets. The team powers reporting, ad-hoc analysis, data science, and increasingly, AI analytics workloads. On a typical day you'll model data from a broad ecosystem of sources on the central data platform, help define and validate key metrics, and ensure the data that models, dashboards, and AI tools depend on is accurate, reliable, and well documented.

Responsibilities

• Own scalable DBT models and datasets that serve as the authoritative source of truth for key business metrics across the organization.
• Design clear data models and grains (dimensions, facts, timeseries, and marts) that analysts and downstream tools can use with confidence.
• Contribute to semantic layer and metric governance, ensuring definitions are consistent, documented, and reliable across reporting surfaces.
• Drive team standards for modeling patterns, testing frameworks, naming conventions, and CI/CD deployment practices.
• Implement data quality and observability strategies that surface issues proactively and build stakeholder trust.
• Collaborate with Data Infrastructure, Engineering, and Analytics teams to improve model performance, runtime, and warehouse efficiency at scale.
• Ensure all data ingestion and modelling adheres to rigorous security and privacy-first standards.
• Mentor junior and intermediate analytics engineers through code review, pairing, and knowledge sharing.

Qualifications

• 5+ years in analytics or data engineering, with 3+ years focused on analytics engineering and production DBT development.
• Expert-level SQL and DBT skills, including advanced modeling patterns, incremental processing, and multi-environment deployment.
• Deep experience with modern cloud data warehouses (e.g. Athena, Snowflake, BigQuery, Databricks, or Redshift), including performance tuning and partitioning.
• Strong understanding of dimensional modeling, metric design, and documenting grains and business logic for consumers.
• Familiarity with semantic layer or metrics tooling (e.g. LookML, MetricFlow, dbt Semantic Layer).
• Hands-on experience with CI/CD for data pipelines and orchestration tools (e.g. Airflow, Dagster, Prefect).
• Bonus: B2B SaaS metrics, event-stream modeling, lakehouse table formats (Iceberg, Parquet), or reverse ETL.