Lead Data Engineer

LogicMonitor•Austin, TX

12d•Hybrid

About The Position

LogicMonitor® is the AI-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise. Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row! The Business Intelligence and Data team transforms complex, global data into strategic assets that inform decision-making across the organization. As a Lead Data Engineer, you will operate at the intersection of architecture and business impact—shaping the data platform, accelerating innovation, and enabling data-driven outcomes at scale. In the Lead Data Engineer role, you will define and drive the technical vision for data engineering, leading the design of scalable, high-impact data platforms, pipelines, and models. You will partner closely with cross-functional stakeholders to align data strategy with business priorities, establish engineering standards, and elevate data quality and accessibility across the enterprise. Acting as a senior technical leader and hands-on architect, you will influence system design, mentor engineers, and deliver solutions that scale with evolving data and analytics needs.

Requirements

6+ years extensive experience designing and scaling data platforms, pipelines, and models in distributed environments
Expert in SQL and Python, driving engineering standards, performance optimization, and best practices
Proven ownership of modern data stack architecture (e.g., Airflow, Snowflake, dbt), including orchestration, transformation, and governance
Strong expertise in cloud-based data systems with a focus on scalability, reliability, and cost efficiency
Experience enabling AI/ML use cases and building data foundations for advanced analytics

Responsibilities

Define and drive the architecture of scalable, reliable data pipelines that power analytics and data products across the organization
Establish and enforce engineering standards to optimize pipelines, frameworks, and data systems for performance, cost efficiency, and developer velocity
Lead the design of data models and transformation layers that convert raw, disparate data into trusted, high-quality, and reusable datasets
Own complex data integration challenges, setting patterns and best practices to ensure consistency, scalability, and resilience across systems
Architect and evolve the organization’s data compute and storage platforms, balancing performance, scalability, and cost at enterprise scale
Set strategy and governance for data security, privacy, and compliance, partnering with security teams to proactively mitigate risk
Define and maintain enterprise data architecture standards, documentation, and reference models to guide long-term platform evolution
Own the long-term strategy and roadmap for the data technology stack (e.g., Snowflake, dbt), aligning platform capabilities with business and product objectives
Identify systemic gaps across infrastructure, tooling, and team processes; drive cross-functional initiatives to resolve root causes and improve engineering effectiveness
Translate business priorities into scalable data platform capabilities, shaping and enabling high-impact data products and workflows
Serve as the senior technical point of contact for data platform vendors, influencing roadmaps, driving feature adoption, and reducing friction for internal stakeholders