Analytics Engineer

Mantis operates three proprietary platforms: Procure (energy procurement and reverse auctions) Perform (the unified client portal for asset management energy rebates and efficiency) and Pacific (our enterprise reporting and analytics platform).

Pacific is the data backbone. It pulls from every internal system and key external sources to power sales operations financial forecasting KPIs and executive scorecards and through ThoughtSpot embedded inside Perform delivers analytics directly to our customers as part of the product. Pacific is also the foundation we built so AI works reliably at Mantis: the data layer where most companies struggle we addressed first.

We are scaling Pacific. The updated customer engagement platform Perform launches June 1. Embedded customer analytics is moving toward tiered paid offerings. Internal demand for trusted reporting is accelerating. Today Pacific is owned by a single analytics engineer; that does not scale. We are hiring an Analytics Engineer to build alongside the platform lead: own production analytics work end-to-end raise the bar on quality and documentation and help shape how AI-assisted analytics engineering operates on top of a real data foundation.

We expect an established practitioner who can ship.

• Build and maintain dbt models on the Pacific Postgres warehouse fed by Stitch and Fivetran
• Develop PowerBI reports and SQL analyses for internal stakeholders: sales operations financial forecasting KPIs executive scorecards
• Build and extend ThoughtSpot Liveboards embedded inside the Perform customer portal delivering Pacific-powered analytics directly to customers
• Partner with Product Sales Operations and Finance to define KPIs across Procure and Perform internal and customer-facing
• Own data quality testing and documentation hygiene across dbt and downstream BI
• Contribute to ELT pipeline reliability source health refresh cadence schema change management
• Use Claude as a core part of your workflow AI-assisted SQL and dbt development data Q&A documentation accelerated report iteration and help define how analytics engineering at Mantis runs AI-forward

• Stitch Fivetran or comparable modern data pipeline tooling
• Postgres
• Power BI and ThoughtSpot (or other embedded analytics - Sisense Looker)
• SQL and Python for data work
• Hands-on experience using Claude or another LLM in analytics workflows
• Exposure to facilities energy real estate or industrial data
• Prior partnership with product engineering and FP&A teams

• Curiosity
• Business acumen
• A bias toward shipping work that actually gets used
• Pacific is not a side project; it is a foundational element of our enterprise.
• Your work will show up in:
  • Board decks
  • Sales pipeline reviews
  • The screens our customers log into every day

• Months 16
  • Own a Pacific reporting domain end-to-end
  • Ship production dbt models and downstream PowerBI or ThoughtSpot deliverables
  • Build relationships across product ops and finance
• Months 612
  • Contribute to architecture and KPI standards alongside the function lead
  • Take point on a cross-functional analytics initiative
• Year 2
  • Develop into a senior analytics engineer and co-lead on Pacific
  • Gain direct visibility to the CTO
  • Have a clear path to broader scope as the team grows

• A data foundation that is real.
  • Pacific has been built deliberately over years.
  • You will not spend your first six months untangling broken pipelines you will build on a working stack and extend it where it matters.
• Customer-facing impact.
  • Pacific data is not just for internal dashboards.
  • It powers the analytics our customers use to make capital and operational decisions on their own portfolios.
• AI without the hype.
  • We use Claude as a working tool every day because the data underneath supports it.
  • You will help define how that operates as a discipline not a demo.
• Scale with backing.
  • Mantis is private equitybacked (Blackstone) and in an active growth phase both organic and through acquisition.
  • Analytics is core to how that scale gets executed.
• Ownership early.
  • This is a two-person function today.
  • The work you do is visible and it matters.