Senior Analytics Engineer
New
1
1PasswordCybersecurity / SaaS
This is a remote opportunity within Canada and the US.Full-TimeSenior
SalaryUSA-based roles only: The annual base salary for this role is between $138,000 USD and $193,000 USD. Canada-based roles only: The annual base salary for this role is between $115,000 CAD and $161,000 CAD.
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Job Details
- Experience
- 5+ years in analytics or data engineering, with 3+ years focused on analytics engineering and production DBT development
- Required Skills
- SQLCI/CDdbt
Requirements
- 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, partitioning, and incremental strategies.
- Strong understanding of dimensional modeling, metric design, and how to document grains and business logic for consumers.
- Familiarity with semantic layer or metrics tooling (e.g. LookML, MetricFlow, dbt Semantic Layer) or equivalent in-repo metric standards.
- Hands-on experience with CI/CD for data pipelines and orchestration tools (e.g. Airflow, Dagster, Prefect).
- Able to communicate complex data concepts clearly to both technical and non-technical audiences.
- Experience with B2B SaaS metrics preferred.
- Experience with event-stream or behavioural data modeling preferred.
- Experience with lakehouse table formats (Iceberg, Parquet) and merge-based incremental loads preferred.
- Experience with reverse ETL, data mesh concepts, or open-source data tooling preferred.
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, and champion adoption.
- 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.
- Evaluate and introduce tooling and methodologies that improve the reliability and scalability of our analytics stack.
- Ensure all data ingestion and modelling adheres to our rigorous security and privacy-first standards.
- Translate ambiguous business requirements into well-scoped technical solutions, serving as a trusted advisor to cross-functional stakeholders.
- Mentor junior and intermediate analytics engineers through code review, pairing, and knowledge sharing.
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