Senior / Staff Analytics Engineer

Position Summary

Fora Financial is hiring its first dedicated Analytics Engineer. You will own dbt, the modeling layer, and the semantic layer that defines how Fora measures its business in code. The foundation you build will make governed reporting and AI-native analytics possible at Fora.

This is a hands-on Senior or Staff IC role on a small, high-trust data team. You will partner closely with the VP of Data & AI, Data Engineering, and Risk & Analytics. You will inherit a dbt project built by an external partner, assess it, and own what it becomes next.

If you treat dbt as production software, enjoy writing Python as much as SQL, and want your modeling decisions to shape how a $4B+ originator understands its business, this role is for you.

**This role is not eligible for visa sponsorship. All applicants must be authorized to work for any employer in the US.

Core Stack

Relevant tools include dbt, SQL, Python, Git, Tableau, Snowflake, and emerging semantic-layer and MCP tooling. Orchestration and semantic-layer choices are still evolving, so strong judgment matters more than checklist familiarity.

A Day in the Life of a Senior / Staff Analytics Engineer

• dbt at Fora: project structure, conventions, CI/CD, tests, contracts, documentation, and performance.
• Data models across bronze, silver, and gold layers.
• The semantic layer (dbt MetricFlow or equivalent): metric definitions, dimensions, governance, and adoption.
• Production operation of the transformation layer: jobs, dependencies, failures, retries, alerts, environments, and release hygiene.
• Python tooling for validation, dbt utilities, lightweight automation, and integrations.
• Data quality, test coverage, and observability for modeled tables.
• Partnership with Risk & Analytics to formalize business metrics currently spread across SQL, Tableau, and analyst knowledge.

What Success Looks Like

• In 6-12 months, you have stabilized and improved the inherited dbt project.
• You have established clear modeling conventions, release hygiene, and documentation standards.
• Core models are more trustworthy through stronger tests, observability, and data quality practices.
• High-value business metrics are defined in a governed semantic layer.
• BI users and AI-native analytics workflows can consume governed metrics with less ambiguity.

What You Have

• Deep dbt experience. You have shipped and operated dbt as production software, not just a SQL wrapper. You have opinions about structure, macros, packages, tests, contracts, and CI.
• Strong dimensional and event-based modeling fundamentals: star schemas, conformed dimensions, slowly changing dimensions, point-in-time logic, grain discipline, and event-based modeling.
• Semantic-layer experience. You have built or owned metric definitions as code using MetricFlow, LookML, Cube, AtScale, or similar.
• Python competence for production analytics: API pulls, validation scripts, data quality checks, dbt utilities, lightweight automation, and debugging. You do not need to be a platform engineer, but you cannot be SQL-only.
• Working knowledge of orchestration: DAGs, dependencies, retries, alerts, incremental processing, logs, SLAs, and deployment environments.
• Git fluency: branches, rebases, conflict resolution, and meaningful commit history.
• Clear stakeholder communication. You can explain trade-offs, say no diplomatically, and maintain standards while building trust.
• Strong writing. You write useful PRs, durable documentation, and decision records other people can follow.
• AI-native curiosity. You are actively learning how LLMs, MCP, and governed semantic layers change a