Data Analytics

Responsibilities

• Design and build production dbt models on Databricks that the organization uses as the source of truth for what’s happening in the business.
• Support external reporting with accurate, governed data that stakeholders and partners can trust.
• Own data quality: testing, CI/CD, documentation, and patterns the team follows.
• Build and evolve Databricks dashboards and semantic metrics for self-service across the business.
• Lead technically (code review, architecture, mentoring junior AEs) so the team ships faster with a higher quality bar.
• Partner with Data Engineering, Analytics Engineering, and analytics stakeholders to turn messy business problems into durable data products.
• Drive AI-native workflows: Cursor/Claude (or equivalent) in daily development; model and doc design that works for LLM consumers.

Requirements

• 5+ years in analytics engineering or equivalent: you’ve owned production models end-to-end, not just ad hoc analysis.
• Expert SQL and deep dbt experience (architecture, incremental models, tests, macros, CI/CD).
• Strong Databricks BI (or equivalent semantic layer) delivery.
• AI-savvy in practice: you already use AI coding tools daily, validate outputs rigorously, and design data for both humans and agents.
• Proven ability to execute in ambiguity: startup pace, incomplete specs, bias toward shipping.
• Clear communicator across technical and non-technical audiences.
• Owner mentality: you see what’s broken, fix it, and raise the bar for others.

Nice-to-Haves

• Fintech/regulated data experience.
• Python automation.
• GitLab/MR workflows.
• Prior tech-lead or lead/senior IC experience on a lean data team.