Software Engineering Manager, Data & Analytics

About the role

In this leadership position, you will define and drive the strategy for how Limble collects, stores, and surfaces data to power customer-facing insights across our platform. You will own the data repository and the reporting framework, establishing the foundation that other stream-aligned engineering teams build upon to surface analytics inside their product areas. This role involves investigating and leveraging the power of AI tooling to make that strategy a reality for our customers and internal operations. You will partner with product leadership to translate customer and business analytics needs into a clear technical roadmap for the team. As a player-coach, you will be close enough to the technical work to make strong architectural decisions and set engineering standards while building and leading the team that executes on the vision. You will own the observability of your data pipelines by defining SLAs for data freshness and quality and building alerting to keep consumers informed when something is off. The role requires establishing data governance and quality standards so the rest of engineering can trust the data they are building on top of.

Key facts

What you'll do

• Define and drive the strategy for Limble's analytical data infrastructure, setting the long-term vision for data collection, storage, and consumption.
• Design the boundary between transactional data stores and the analytical layer, ensuring both remain performant and maintainable as the platform scales.
• Architect and own the reporting framework that stream-aligned engineering teams use to embed analytics into their product areas, enabling rapid feature development.
• Partner with product leadership to translate customer and business analytics needs into a concrete, executable technical roadmap.
• Build, hire, and lead a high-performing team of data and analytics engineers, fostering a culture of quality and ownership.
• Drive significant architectural decisions through ADR reviews with Principal and Staff engineers, bringing well-reasoned proposals and leading the technical conversation.
• Own the observability of your data pipelines by defining SLAs for data freshness and quality, building alerting mechanisms, and keeping stakeholders informed of issues.
• Establish data governance and quality standards so the rest of engineering can trust the data they are building on top of and reduce redundant effort.
• Drive adoption of the reporting framework across stream-aligned teams, ensuring consistent patterns and maximizing reuse of analytics infrastructure.
• Stay hands-on enough to review critical design decisions, contribute to architecture, and help your team get unstuck on complex technical challenges.
• Evaluate and integrate emerging AI capabilities into the data and analytics workflows to enhance productivity and insight generation.
• Collaborate with cross-functional partners to prioritize initiatives that deliver the highest impact on customer and business outcomes.
• Mentor engineers on data modeling, ELT/ETL pipeline design, and building analytics-ready datasets that serve diverse consumer needs.
• Ensure all data platforms and pipelines adhere to security, compliance, and operational best practices required for a scaling B2B SaaS company.
• Continuously assess tooling and processes, driving improvements that simplify complexity and improve reliability for analytics consumers.

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, specifically leveraging services such as Aurora PostgreSQL, DynamoDB, Redshift, S3, and related services.
• Strong understanding of when to use an operational data store versus an analytical one, and how to design the pipeline that connects the two effectively.
• Strong background in data modeling, ELT/ETL pipeline design, and building analytics-ready datasets that serve a variety of consumer needs.
• Experience building or contributing to a reporting or analytics framework that is consumed by multiple engineering teams or product surfaces.
• Experience owning data pipeline observability, including monitoring, alerting, SLAs, and incident response for data freshness and quality issues.
• Actively leveraging AI coding tools such as GitHub Copilot, Cursor, or Claude in day-to-day development and setting the expectation for the team to adopt these tools.
• Some exposure to embedding AI-driven capabilities inside of a SaaS product, integrating intelligent features into user-facing workflows.
• Comfortable operating in a player-coach capacity, able to write a design doc, review a schema, or weigh in on query optimization alongside strategic work.
• Track record of hiring and developing engineers, with the ability to identify strong data engineers and motivate them toward the mission at Limble.
• Strong communicator who can translate complex data architecture decisions into language that product and business stakeholders can understand and act on.
• Demonstrated bias toward simplicity over complex solutions, favoring maintainable approaches that deliver clear value quickly.
• Located in or near the Charlotte, NC metro area, allowing for collaboration in the primary work location.

Nice to have

• Experience with embedded or in-app analytics, focusing on surfacing insights directly inside a SaaS product rather than only internal BI.
• Familiarity with event streaming on AWS, including technologies such as Kinesis, MSK, EventBridge, or Kafka.
• Experience with AWS Glue, Athena, or Lake Formation for data pipeline and data lake operations.

Practical notes

This role is full-time and based in the Charlotte, NC metro area. No additional benefits, visa sponsorship, or travel requirements are specified in the source material.