Analytics Engineering Manager, Data Platform & Governance

We are looking for a hands-on Analytics Engineering Manager to take ownership of data governance, quality, and platform reliability across a multi-brand business. You will define and drive the data platform roadmap, ensuring that source data, pipelines, metrics, dashboards, and AI-powered tools remain accurate, secure, and trustworthy. Working across product, marketing, operations, and finance, you will translate business needs into clear priorities and scalable governance practices. You will also lead the ongoing migration to Lightdash, strengthen the semantic layer, and establish consistent event-tracking standards. This is a build-oriented leadership role: you will begin as an individual owner, combining technical execution with strategic planning, before growing the team as the function expands. You will have the opportunity to shape the foundations, standards, and processes that enable the entire organization to make better decisions through data.

• Proven experience in analytics engineering, data engineering, data platform management, or a closely related discipline, with hands-on responsibility for data quality and governance.

• Previous experience managing or being accountable for other people's work, including setting priorities, allocating responsibilities, and overseeing delivery.

• Strong technical expertise in data warehouses and pipeline technologies, particularly Amazon Redshift, dbt, and Airflow.

• Practical experience with data ingestion tools such as Fivetran and custom Airflow pipelines.

• Experience managing BI platforms and governance practices at company scale, including workspace administration, permissions, reporting standards, and self-service enablement.

• Familiarity with event-tracking platforms such as Segment, including event catalogs, naming conventions, property dictionaries, review processes, and tracking quality controls.

• Understanding of data lineage, schema changes, impact analysis, freshness monitoring, anomaly detection, and automated data quality checks.

• Ability to establish data contracts and collaborate with engineering teams to prevent breaking changes from affecting downstream systems.

• Experience with semantic layers and metric governance, ensuring consistent definitions and reliable business reporting.

• Strong SQL skills and the ability to investigate data issues, debug pipelines, configure platform permissions, and implement technical solutions directly.

• An automation-first mindset, with experience building monitoring and quality controls rather than relying primarily on manual reviews and checklists.

• Daily familiarity with AI tools such as Claude Code, Copilot, or ChatGPT for coding, automation, anomaly investigation, and documentation.

• Understanding of the governance requirements associated with AI-powered data consumption, including making warehouse information safe, consistent, and understandable for AI agents.

• Strong product and business orientation, with the ability to turn ambiguous stakeholder requests into a prioritized technical roadmap.

• Excellent communication and stakeholder-management skills, including the ability to establish standards across teams you do not directly manage and address non-compliance constructively.

• Comfortable working independently in a remote, asynchronous environment and building governance capabilities that are still evolving.

• Experience with every tool listed is not required; hands-on depth in the warehouse and pipeline layer, together with credible experience maintaining BI and event-tracking standards at organizational scale, is essential.