Analytics Engineer

Role overview

Own and improve the analytics data stack so teams can rely on consistent information in dashboards and natural-language queries. The work combines scalable data modeling, semantic definitions, and carefully governed AI workflows, with close collaboration across technical and business teams.

Responsibilities

- Build and optimize reusable dbt models, macros, and packages, with tests that protect data integrity and performance. - Define and maintain metrics, dimensions, and documentation in a semantic layer that supports consistent reporting and natural-language access. - Add LLM-powered transformations, such as text classification or record enrichment, with evaluation methods, cost and latency monitoring, and fallbacks. - Develop AI-assisted workflows for modeling, documentation, testing, code review, and incident triage, including suitable review and quality controls. - Monitor data refreshes and maintain models used for business intelligence and company reporting. - Work with product, engineering, and BI colleagues; explain technical data issues in business terms and support teammates in responsible use of AI tools.

Requirements

- Deep experience using SQL and dbt to build and maintain scalable data models. - Experience defining metrics, entities, and business logic in a semantic modeling layer, with an understanding of reliable natural-language querying. - Practical experience applying LLMs to data workflows and evaluating, testing, and governing outputs that may vary. - Familiarity with cloud platforms such as AWS or GCP, plus AI coding assistants or agent-based tools. - Strong focus on data quality, repeatable workflows, documentation, and appropriate human review. - Ability to understand business needs, communicate clearly with nontechnical stakeholders, and collaborate across teams.

Benefits and work setup

The listing describes remote work and mentions salary and benefits, but provides no specific compensation or benefit details.