Software Engineering Manager, Data & Analytics

Limble is revolutionizing the way businesses manage their maintenance operations by providing a comprehensive suite of software solutions. They are seeking a Software Engineering Manager for Data & Analytics to lead the strategy and execution of data collection, storage, and insights across their platform, while also building and managing a high-performing team.

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 solutions
• Located in or near the Charlotte, NC metro area
• Experience with embedded or in-app analytics (surfacing insights directly inside a SaaS product, not just internal BI)
• Familiarity with event streaming on AWS (Kinesis, MSK, EventBridge, Kafka, or similar)
• Experience with AWS Glue, Athena, or Lake Formation for data pipeline and lake orchestration
• Experience at a B2B SaaS company in the 50–200 employee range
• Exposure to regulated software environments (SOC 2, HIPAA, or similar)
• Experience with BI or visualization tooling (Metabase, Looker, Tableau, or similar) in a product context