Lead Analytics Engineer

We're consolidating analytics onto a single SQL Server database that sits right alongside our lending systems, and you'll own it end to end: what lands in it, how it's modeled, and the reporting that runs off it. The goal is self-service — governed tables, a real data dictionary, and published models so that most questions answer themselves. When someone does need you directly, the result should be a reusable asset, not a one-off number.

This is a hands-on job for someone who likes to finish things and keep them running. You'll inherit a working platform and a team that has already solved the hard migration problems. From there, the work is new sources, shared definitions, better distribution, and a roadmap you help write.

• →Run the analytics database and its scheduled jobs - reliable, monitored, and documented.
• →Pull in new sources through vendor APIs (CRM, contact center, lending platforms) with incremental, restartable laods
• →Model raw data into tables and views the business can trust.
• →Build operational reporting that has to be right every day: call lists, merchant partner reporting, and management reports.
• →Own the data dictionary and KPI definitions in partnership with business owners.
• →Lead one analyst, setting direction and raising the bar on quality.

• 5+ years running production data pipelines and reporting.
  
• Strong SQL Server: window functions, dedup, and performance on big tables.
  
• Python and API extraction and pipelines.
• Experience operating scheduled jobs and logging, alerting, and quality checks.
• You've published data to a BI tool and supported the people using it.
• Comfortable working in Git.

• Consumer lending or financial services experience.
  
• Salesforce or Genesys Cloud Data
• Tableau, Metabase, ThoughtSpot, Omni, or similar
• dbt, AZURE SQL, or AI/LLM tooling on data.

• Learn the business and the current platform.
• Operate - take over daily operations of the platform alongside the team.
• Deliver - land one new source, end to end.
• Define - get the first shared KPI definitions agreed and written down.