Senior Analytics Engineer

Responsibilities

• Drive data warehouse strategy and performance, including modeling standards, materialization strategy, query performance, and speed and cost optimization.
• Own Silver- and Gold-layer modeling in dbt by building documented, tested, and governed models.
• Consolidate redundant pre-materialized views, resolve metric drift, and correct non-additive measures at risk of incorrect re-aggregation.
• Build and govern the semantic layer as a single source of truth for metric definitions.
• Lead BI tooling strategy and enablement, standardizing the BI stack and building governed data products for self-service analytics.
• Partner with stakeholders and analysts to translate business questions into durable models and metrics.
• Establish governance standards and tooling that enable upstream teams to own domain data effectively.
• Troubleshoot data quality and consistency issues and drive root-cause, long-term fixes.
• Identify opportunities to optimize, refactor, and scale analytics infrastructure.
• Stay informed about modern data-stack developments and introduce tools and processes that improve workflows.

Requirements

• 5–7+ years of data or analytics engineering experience, including at least 2 years in a senior capacity.
• Expert proficiency with SQL and data modeling tools such as dbt, Dataform, or SQLMesh, including modeling, testing, documentation, and macros.
• Strong command of dimensional modeling and medallion or layered architectures.
• Hands-on experience with at least one cloud data warehouse, such as Snowflake or BigQuery, including performance and cost optimization.
• Experience with a semantic or metrics layer, such as dbt Semantic Layer, MetricFlow, Cube, or LookML, and a track record of governing metric definitions.
• Proficiency with modern BI tools such as Hex, Sigma, Omni, Tableau, Looker, Metabase, or Lightdash.
• Working proficiency with Python for transformation, tooling, and automation.
• Strong proficiency with Git and version-control practices.
• Demonstrated fluency with AI-assisted development tools.
• Experience working with modestly sized, fast-paced teams.
• Strong communication skills and the ability to partner across engineering, product, and business stakeholders.
• Working knowledge of iterative, value-focused technical delivery.

Nice to Have

• Familiarity with digital marketing data or the multifamily or real estate industry.
• Experience contributing to a data warehouse migration, such as Snowflake to BigQuery or vice versa.
• Experience operating a semantic layer or large dbt project at scale, including metric governance and drift remediation.
• Experience with BI write-back, reverse ETL, or finance-focused analytics.

Compensation and Benefits

• Prolonged periods of desk-based computer work.
• Ability to lift up to 15 pounds at times.