Analytics Engineering Manager, Data Platform & Governance

Accountabilities: Own the data platform and governance roadmap, identifying business needs across product, marketing, operations, and finance and translating them into prioritized initiatives. Balance stakeholder requests with platform reliability, data quality, security, and long-term scalability, communicating priorities and trade-offs clearly. Establish automated monitoring for data quality and freshness across source systems, pipelines, warehouse models, and reports. Detect upstream schema and source-data changes before they affect downstream systems, and lead incidents through investigation and resolution. Build and maintain end-to-end data lineage connecting production sources, warehouse models, metrics, and BI dashboards. Develop impact assessment processes so engineering teams understand downstream data dependencies before releasing production changes. Work toward implementing data contracts and integrating breaking-change detection into engineering workflows. Lead the Lightdash migration, defining workspace structure, permissions, naming conventions, certification standards, and user enablement. Maintain a well-organized BI environment that supports stakeholder self-service while ensuring query performance and responsible warehouse costs. Expand and govern the semantic layer, ensuring consistent, documented definitions for business-critical metrics and preventing uncontrolled metric duplication. Own Segment event tracking governance, including the event catalog, naming standards, property dictionary, review processes, and automated detection of tracking drift. Govern data access for AI tools and agents, ensuring warehouse information is documented, consistent, secure, and suitable for reliable AI-driven queries. Oversee data security and privacy practices, including access controls, personally identifiable information (PII) handling, retention policies, and periodic access reviews. Build the documentation, ownership structures, and review processes required to make governance sustainable and independent of individual contributors. Remain actively involved in technical delivery, writing SQL, debugging Airflow pipelines, configuring permissions, and developing automation and quality checks. Initially operate as an individual contributor, then help recruit and lead a Lead Analytics Engineer as the function expands. Mentor team members, coordinate priorities, and establish standards that encourage accountability and collaboration across teams. Requirements: Proven experience in analytics engineering, data engineering, or data platform management, with responsibility for data quality, governance, and technical delivery. Previous experience managing or leading others, including prioritizing work, allocating responsibilities, and maintaining accountability for team outcomes. Strong hands-on expertise with data warehouses and pipeline technologies, particularly Amazon Redshift, dbt, and Airflow. Practical experience maintaining production data pipelines, troubleshooting failures, and implementing automated monitoring and validation. Credible experience governing a business intelligence platform and maintaining reliable event-tracking standards at company scale. Experience with BI tools such as Lightdash, Tableau, or Metabase, including workspace administration, permissions, metric definitions, and dashboard governance. Familiarity with Segment or comparable event-tracking platforms, including event catalogs, naming conventions, property standards, and tracking quality. Strong understanding of data lineage, impact analysis, semantic layers, metric governance, and data contracts. Experience designing automated systems for freshness monitoring, anomaly detection, schema-change identification, and data quality validation. Practical knowledge of data security, access management, PII handling, and privacy requirements. Regular use of AI-powered development tools such as Claude Code, GitHub Copilot, ChatGPT, or similar solutions to accelerate coding, automation, anomaly investigation, and documentation. Understanding of how AI agents consume enterprise data, including the importance of making warehouse information structured, documented, secure, and machine-readable. Strong SQL skills and the ability to personally investigate and resolve technical issues across pipelines, permissions, and data models. Product-minded approach, with the ability to translate broad business requests into a clear roadmap and explain technical priorities and trade-offs. Excellent communication and stakeholder management skills, including the ability to uphold standards across teams without direct management authority. Automation-first mindset, favoring scalable monitoring and preventive controls over manual reviews and repetitive checklists. Comfortable building governance practices and processes from an early stage rather than simply maintaining an established system. Nice to have: Experience with Fivetran, custom Airflow ingestion pipelines, and broader data observability solutions. Nice to have: Experience scaling AI-powered analytics engineering agents and integrating data governance into AI workflows. Benefits: Annual base salary of USD 75,000–120,000. Fully remote working arrangement. Flexible paid time off, with an emphasis on outcomes and individual responsibility. Asynchronous collaboration and flexibility to organize a focused working environment. Opportunity to establish and lead a new data governance function. Hands-on technical ownership of a modern analytics and data platform. Opportunity to shape AI-ready data infrastructure and influence how teams use trusted business data. Potential to build and grow a team as the function and business needs expand.