À propos de Kim
With 3 years of experience in analytics engineering as a part of a freelance collective, I help organizations build scalable, transparent, and reliable analytics ecosystems that empower business teams to make confident, data-driven decisions.
As a freelancer, I focus on:
- Transforming legacy SQL workflows into modular dbt models that are testable, maintainable, and well-documented.
- Designing semantic layers and dimensional data models in dbt to support consistent and intuitive reporting across teams.
- Driving self-service analytics adoption by aligning business logic with curated datasets in Looker.
- Promoting data governance and trust through dbt testing, documentation, and naming conventions.
- Supporting the implementation of dbt Mesh to enable domain-based ownership while maintaining shared modeling standards.
- Partnering with stakeholders to define KPIs and business metrics, accelerating the delivery of high-value dashboards and insights.
- Advocating for analytics engineering best practices, enabling teams to scale from ad hoc reporting to structured, governed analytics operations.
My goal is to bridge the gap between raw data and actionable insight—delivering clarity, trust, and speed in every analytics project.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- AccorAnalytics EngineerHÔTELLERIEavril 2024 - mai 2026 (2 ans et 1 mois)Issy-les-Moulineaux, France
Collaborative Analytics Development with dbt and Data Mesh Principles
- Led the migration of legacy Snowflake stored procedures into modular, documented dbt models, improving clarity, maintainability, and team onboarding.
- Contributed to a multi-team dbt architecture (data mesh approach), separating core models and team-owned transformations to ensure autonomy and standardization.
- Defined data contracts and dbt model ownership across teams, supporting scalable collaboration and reducing conflicting definitions of key metrics.
Data Quality and Observability for Analysts and Stakeholders
- Enabled data observability using Elementary, monitoring test results and dbt runs to proactively surface issues in dashboards and datasets.
- Advocated for a "trust but verify" approach to reporting by embedding test coverage stats and freshness alerts in shared dashboards.
- Piloted a centralized metadata catalog to improve discoverability and understanding of dbt models and downstream dashboards.
Semantic Layer and Dashboard Reliability
- Designed and tested semantic models using the dbt Semantic Layer and Cube, exploring consistent metric definitions across tools like Tableau and Looker Studio.
- Partnered with BI developers to validate semantic definitions against existing reports, ensuring consistent logic across tools.
Analytics Enablement and Best Practices
- Delivered internal training workshops on dbt for data and analytics teams, helping standardize modeling practices and improve team-wide SQL quality.
- Shared dbt tips, model patterns, and documentation practices, leading to measurable gains in delivery speed and model reuse across teams.
- PrimelisAnalytics EngineerAGENCE & SSIInovembre 2023 - avril 2024 (5 mois)Levallois-Perret, France
Scalable Analytics Models for Marketing Insights
- Developed a reusable dbt package to deliver cross-platform marketing insights from sources like Google Ads and Search Console, rolled out across 80+ client accounts including Smartbox, La Redoute, and UNICEF.
- Built dashboard-ready dbt models for key marketing metrics (e.g., impressions, CTR, conversion rates), integrated with a custom frontend for real-time analysis.
- Supported business teams with consistent, automated reporting logic, reducing time spent on manual analysis.
Data Quality Monitoring and Process Standardization
- Enabled automated anomaly detection and data quality alerts using Elementary to monitor model test results and freshness, ensuring reliability in client-facing dashboards.
- Promoted data modeling best practices using dbt_project_evaluator, ensuring consistency and maintainability across dozens of client implementations.
Client-Facing Insights Platform & Model Optimization
- Partnered with product and BI teams to integrate dbt outputs into a user-facing analytics dashboard, providing timely and actionable marketing insights to non-technical users.
- Optimized performance across 80+ dbt projects (each with ~150 models), improving model run times and enabling faster iteration for reporting enhancements.
Analytics Tooling Exploration
- Led PoCs for next-generation tools like SQLMesh (as a potential dbt alternative), and contributed to platform evaluation for dashboard scalability and version control.
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