Software Engineering Manager, Data & Analytics

About Limble

At Limble we empower the unsung heroes who support the world. We’re revolutionizing the way businesses manage their maintenance operations by providing a comprehensive suite of software solutions that empower organizations to optimize asset performance and drive operational excellence. From preventive maintenance to inventory management and beyond, our robust CMMS platform offers a suite of features designed to streamline operations and enhance productivity.

About the role

We're building a dedicated data and analytics capability inside our engineering organization, and we need the right person to lead it. As the Software Engineering Manager for Data & Analytics, you'll own the strategy and execution for how Limble collects, stores, and surfaces data to power customer-facing insights across our platform. You’ll be investigating and leveraging the power of AI tooling to help make that strategy a reality for our customers.

You and your team own the data repository and the reporting framework. These are the foundation other stream-aligned engineering teams build on to surface analytics inside their product areas. Your job is to make that foundation so good that teams can ship insights features without reinventing anything. This is a player-coach role meaning that you'll be close enough to the technical work to make strong architectural decisions and set engineering standards, while also building and leading the team that executes on the vision.

You'll start with a small team and grow it. That means you have opinions on hiring, on data architecture, and on what "quality delivery" looks like for analytics infrastructure at a scaling B2B SaaS company. You won't need someone telling you how to build, instead you'll come in ready to define the architecture and get going.

Responsibilities

• Define and drive the strategy for Limble's analytical data infrastructure

• Design the boundary between transactional data stores (what the product reads and writes against) and the analytical layer (what feeds reporting and ai-powered insights), keeping both performant and maintainable

• Architect and own the reporting framework that stream-aligned engineering teams use to embed analytics into their product areas

• Partner with product leadership to translate customer and business analytics needs into a technical roadmap

• Build, hire, and lead a high-performing team of data and analytics engineers

• Drive significant architectural decisions through ADR reviews with Principal and Staff engineers, bringing well-reasoned proposals and leading the conversation

• Own the observability of your data pipelines by defining SLAs for data freshness and quality, build alerting, and keep your consumers informed when something's off

• Establish data governance and quality standards so the rest of engineering can trust the data they're building on top of

• Drive adoption of the reporting framework across stream-aligned teams

• Stay hands-on enough to review critical design decisions, contribute to architecture, and help your team get unstuck

Requirements

• 5+ years of experience in data engineering, analytics engineering, or a related discipline with at least 2 years in an engineering management or technical lead role

• Proven experience designing data infrastructure on AWS (Aurora PostgreSQL, DynamoDB, Redshift, S3, and related services)

• Strong understanding of when to use an operational data store vs. an analytical one, and how to design the pipeline between them

• Strong background in data modeling, ELT/ETL pipeline design, and building analytics-ready datasets

• Experience building or contributing to a reporting or analytics framework consumed by multiple engineering teams or product surfaces

• Experience owning data pipeline observability such as monitoring, alerting, SLAs, and incident response for data freshness and quality issues

• Actively leveraging AI coding tools (GitHub Copilot, Cursor, Claude, or similar) in day-to-day development and sets the expectation for the team to do the same

• Some exposure with embedding AI driven capabilities inside of a SaaS product

• Comfortable staying player-coach: you can write a design doc, review a schema, or weigh in on a query optimization

• Track record of hiring and developing engineers; you know what a good data engineer looks like and can get them excited about our mission here at Limble

• Strong communicator who can translate data architecture decisions into language product and business stakeholders actually understand

• Bias toward simplicity over complex soluti