Senior / Staff Analytics Engineer

Role Overview

Fora Financial is hiring its first dedicated Analytics Engineer (Senior or Staff) to own the analytics modeling and semantic layers. This is a hands-on IC role on a small, high-trust data team, partnering closely with the VP of Data & AI, Data Engineering, and Risk & Analytics.

Key Responsibilities

• Own dbt end-to-end: modeling layer, semantic/metric definitions, governance, and adoption.
• Inherit and improve an existing dbt project built by an external partner: assess, stabilize, and evolve it.
• Treat dbt as production software, including:
  • project structure, conventions, CI/CD, tests, contracts, documentation
  • performance and release hygiene
• Build and maintain data models across bronze / silver / gold layers.
• Operate transformation jobs reliably: dependencies, failures, retries, alerts, environments.
• Develop Python tooling for validation, dbt utilities, lightweight automation, and integrations.
• Improve data quality, test coverage, and observability for modeled tables.
• Partner with Risk & Analytics to formalize business metrics currently spread across SQL, Tableau, and analyst knowledge.
• Drive adoption of governed metrics for BI and AI-native analytics workflows.

What Success Looks Like (6–12 months)

• Stabilized and improved the inherited dbt project.
• Established modeling conventions, release hygiene, and documentation standards.
• Improved trust in core models via stronger tests, observability, and data quality.
• Defined high-value business metrics in a governed semantic layer.
• Enabled BI and AI-native workflows to consume metrics with less ambiguity.

Requirements

• Deep dbt experience; shipped and operated dbt as production software (not just SQL wrappers).
• Strong dimensional/event-based modeling fundamentals:
  • star schemas, conformed dimensions, SCDs
  • point-in-time logic, grain discipline, event-based modeling
• Semantic layer experience (e.g., MetricFlow or equivalent).
• Python competence for production analytics tasks (validation scripts, quality checks, automation, debugging).
• Working knowledge of orchestration (DAGs, retries, alerts, incremental processing, logs, SLAs, environments).
• Git fluency (branches, rebases, conflict resolution, meaningful history).
• Strong written communication (PRs, documentation, decision records) and stakeholder communication.
• AI-native curiosity and willingness to apply LLM/MCP/semantic-layer learning responsibly.

Nice to Have

• Lending or financial-services domain experience.
• Comfort using LLMs to accelerate SQL, tests, documentation, and boilerplate while maintaining strong engineering judgment.