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.