Senior Analytics Engineer

Details

• 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 (subscription revenue, customer lifecycle, usage and adoption)
• Event-stream or behavioural data modeling; SCD and snapshot patterns
• Lakehouse table formats (Iceberg, Parquet) and merge-based incremental loads
• Reverse ETL, data mesh concepts, or open-source data tooling contributions
• 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