About the role
MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100 , the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the talent to match. About MaintainX MaintainX is the world's leading mobile-first work execution platform for industrial and frontline teams. We help over 14,000 customers — including Duracell, McDonald's, Shell, DHL, and Volvo — reduce unplanned downtime and run more efficient operations. In July 2025 we closed a $150M Series D led by Bessemer Venture Partners , bringing our total funding to $254M at a $2.5B valuation . We were named to the 2025 Forbes Cloud 100 and ranked #1 in EAM and CMMS on G2's Summer 2025 report. The market we serve is changing fast: 68% of industrial companies have the same or more downtime than they did in 2024, and there are 3.8M unfilled manufacturing jobs projected through 2033. Our data team powers the decisions that help frontline workers and the organizations that rely on them. This role sits at the intersection of data modeling, AI-native analytics, and cross-functional enablement.
The Role You'll build the data foundation that enables every team at MaintainX — from product to operations to AI — to trust and use our data independently. This is a hands-on analytics engineering role where quality, scalability, and self-serve access are the actual deliverables. Build extensible data models that support product analytics, internal reporting, and AI-native workflows at scale Implement data quality checks, validation rules, and governance policies that keep our data accurate and compliant Develop and evangelize analytics engineering tooling that enables stakeholders to build their own models while adhering to best practices Monitor and optimize transformation pipelines and storage for performance, scalability, and cost efficiency Create and maintain comprehensive documentation so datasets are discoverable and usable without hand-holding You'll work closely with engineers, product managers, and business teams across the org. Success at 6–12 months looks like: stakeholders building self-serve confidently, pipelines running cleanly, and the data team spending less time answering "where does this number come from?" About You Must-haves: 4+ years as an analytics engineer building and orchestrating pipelines using dbt and data lakehouse technologies (Databricks or similar) Deep knowledge of dbt, data modeling, and BI best practices — you have a point of view and defend it Strong business and product acumen; you can take an analytics requirement from a non-technical stakeholder and turn it into a self-serve solution Hands-on experience applying software development fundamentals: environment segregation, version control, separation of concerns, release management Track record of technical mentoring and raising the output of the people around you — including non-technical partners Nice-to-haves: Experience with AI-augmented analytics engineering workflows Experience applying analytics engineering to governance and compliance frameworks (anonymization, pseudonymization, data masking) We Offer Competitive base + equity in a post-Series D company at $2.5B valuation Day-1 health, dental, and vision coverage (Canada Life for Canada; TriNet/BCBS for US) Unlimited PTO — we actually take it $500 home office stipend · $1K annual L&D budget Hybrid hubs in Montreal, Raleigh, Miami, and San Francisco (or fully remote per role)
