Analytics Engineer

United States·HoustonFull-timemid

Data EngineerData

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Quick Summary

Overview

Mantis Innovation provides managed facility services and turnkey program management with technology-enabled solutions that target the entire building footprint.

Technical Tools

Data EngineerData

Mantis Innovation provides managed facility services and turnkey program management with technology-enabled solutions that target the entire building footprint. We look at the entire facility—inside and out—and can impact 70% of what a building operator allocates critical budget dollars toward, including: Strategic electricity and natural gas procurement, renewable energy, and demand response; climate impact reduction and reporting, net zero strategies, and sustainability planning; roofing, solar, HVAC assessment management, pavement, building envelope, data center optimization, and EV charging; lighting/LED retrofits, HVAC/mechanical systems, and BMS/BAS improvements and implementation.

Mantis is a tech-enabled facilities, energy, and sustainability partner to enterprise and upper-mid-market organizations with complex, multi-site portfolios. We combine advisory services, proprietary technology, and turnkey execution to help clients reduce operating costs, manage risk, and improve building performance.

Three things make that work: deep domain expertise, an unusual operating model that pairs advisory with execution, and a serious investment in data and technology. Mantis is a data-hungry company. Data is not a side function here it is what fuels how we run the business internally and how customers experience value externall

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

4–6 years of analytics or data engineering experience
Strong SQL and database experience
Dimensional modeling fundamentals
Production dbt experience (models, tests, documentation)
Comfort with git-based development and code review
A track record of shipping BI dashboards or embedded analytics to non-technical audiences
Clear written communication; able to explain analytical decisions to business stakeholders

Nice to Have

~2 min read

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 1–6

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 6–12
- 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 equity–backed (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.