# Senior Analytics Engineer

**Company:** [Digible](https://hotfix.jobs/companies/digible)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 5+ years
**Skills:** SQL, dbt, dataform, sqlmesh, Snowflake, BigQuery, Python, Git, semantic layer, metricflow, cube, lookml, Tableau, Looker, Fivetran
**Posted:** 2026-08-05

> Owns Silver- and Gold-layer data modeling, semantic-layer governance, warehouse performance, and BI enablement for trusted self-service analytics. The role requires 5–7+ years of analytics engineering experience, strong SQL and dbt expertise, cloud warehouse optimization, and cross-functional stakeholder partnership.

## Job Description

## 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.

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