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.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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