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Elodie BouquetEB

Elodie Bouquet

Lead Data | Senior Analytics Engineer | SQL Expert

€700/day
Mougins, FR
8-15 years

Average response time: 1 hour

Freelancer profile translated to English.
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About Elodie

Data expert with 9 years of experience, I transform your data into business strategy.

đŸ’Ș MY 5 KEY SKILLS

SQL Expert - Extreme query optimization & large volumes

Modern Data Architecture - dbt, Data Warehouse, GDPR

Performant BI Dashboards - Superset, Microstrategy, Tableau; Power BI

Data Governance & Compliance - Multi-client, GDPR

Strategic Data Consulting, with real business impact

📊 MY ACHIEVEMENTS

- Aviation/Transport: Data strategy, monthly report to demonstrate project value (revenue, customer satisfaction), cross-plane visibility
- SaaS Platform: Modern architecture + DB refactoring + Customer reporting creation
- Private Banking: SQL optimization on very large volumes, data reliability, KPI construction
  • French

    Native or bilingual

  • English

    Native or bilingual

  • German

    Basic

Can work on-site
Mougins (up to 50km), Lyon (up to 10km), Saint-Étienne (up to 10km), Avignon (up to 30km), Nümes (up to 30km)

Experience

  • Vianeo
    Senior Data Analyst
    SOFTWARE PUBLISHING
    September 2022 - July 2025 (2 years and 10 months)
    Valbonne, France
    📌 MISSION

    Complete refactoring of the data architecture and implementation of the analytical strategy to support the growth of the member area.

    đŸ’Ș WHAT I DID

    1. Refactoring of the "Projects" database
    → Management of a Graph legacy
    → Data optimization (snowflake architecture)

    2. Building Apache Superset dashboards
    → Extreme SQL optimization to meet Superset dataset <1min constraint
    → Business needs gathering + optimized visualizations

    → Support for the business team

    3. DESIGN OF A MODERN & SCALABLE DATA ARCHITECTURE
    → Extraction from 30 MariaDB databases → MariaDB Data Warehouse
    → Transformation with dbt (transformations, documentation, automated tests)
    → DigDash data cubes with GDPR/multi-client management
    → Cross-client aggregation for enterprise dashboards

    📊 MEASURABLE RESULTS

    ✅ Performance: Queries going from 2+ minutes to <1 minute
    ✅ Scalability: Architecture capable of supporting 10x growth
    ✅ Governance: Multi-client GDPR system, complete audit trails
    ✅ Documentation: dbt auto-generates transformation documentation
    ✅ Quality: Automated tests on data uniqueness, data freshness

    đŸ› ïž TECHNOLOGIES USED

    - Advanced SQL (constants, indexing, partitioning)
    - Apache Superset (performant dashboards, BI)
    - dbt (data transformation, documentation, tests)
    - MariaDB (design, optimization, DWH)
    - DigDash (data cubes, visualizations)
    - GDPR/Governance (multi-tenancy, audit)

    🎯 IMPACT

    Implementation of a modern data architecture that allows Vianeo to:
    - Serve its clients with reliable and performant data
    - Scale the infrastructure without refactoring
    - Automatically document and test transformations
    - Comply with GDPR governance

    Note: Deployment stopped before completion due to economic context.
    SQL MariaDB Apache Superset Data Governance GDPR
  • SUPRALOG
    IT Product Owner / Data Analyst
    SOFTWARE PUBLISHING
    June 2020 - June 2022 (2 years)
    Antibes, Provence-Alpes-Cîte d’Azur, France
    CREATION OF THE "MEMBER AREA" DATABASE

    🎯 THE CHALLENGE
    Supralog needed a database to:
    - Manage members and their data
    - Support vacation rental bookings
    - Manage inventory and stock
    - Migrate from a legacy system

    Complexity: Multiple data types, many business stakeholders, no room for error.

    💡 MY SOLUTION

    Phase 1: UNDERSTANDING NEEDS
    - Detailed interviews with the business
    - Documentation of existing workflows
    - Identification of hidden needs
    - Writing complete functional specifications

    Phase 2: DATABASE DESIGN
    - Data modeling (members, bookings, inventory)
    - Schema design (tables, relationships, constraints)
    - Migration plan from the old system (data import)
    - Architecture for future scalability

    Phase 3: IMPLEMENTATION & MIGRATION
    - Creation of the production database
    - Data injection (strict validation)
    - Exhaustive testing (data quality)
    - Training for the business team

    Phase 4: SUPPORT & MAINTENANCE
    - Analysis and resolution of data bugs
    - Regular updates
    - Data quality checks
    - Continuous support for the team

    📊 RESULTS

    ✓ Production-ready database at launch
    ✓ 0 critical errors after go-live
    ✓ Business team autonomous in data management
    ✓ System capable of supporting growth
    ✓ 100% adoption rate (satisfied team)

    đŸ› ïž TECHNOLOGIES

    SQL | Database Design | Data Migration | Project Management

    đŸ’Œ KEY LEARNINGS

    1. Specifications are THE foundation (garbage in = garbage out)
    2. Project management is 50% of a data analyst's job
    3. Data injection = very important but often underestimated
    4. Business/technical communication = critical success factor

    👉 IF YOU HAVE A SIMILAR NEED...
    Database construction, data migration, or post-launch support?
    Contact me to discuss.
    SQL Project Management Data Migration Database Design
  • SUPRALOG
    IT Business Analyst for Air France
    AVIATION AND AEROSPACE
    June 2018 - May 2020 (1 year and 11 months)
    Sophia Antipolis
    AIR FRANCE-KLM - JUSTIFYING THE VALUE OF A BACKEND TEAM THROUGH DATA

    🎯 THE CHALLENGE
    Air France-KLM had a classic problem:
    "Why invest in this backend team? What is the ROI?"

    The NBA (Next Best Action) project: API/Web service to personalize offers
    to customers. But was it really profitable? Was it worth maintaining
    a full team?

    💡 MY SOLUTION

    Instead of just saying "it's good," I quantified the impact:

    Phase 1: IN-DEPTH ANALYSIS
    - CTR & Conversion Rates of NBA offers
    - Sales projections "with NBA" vs "without NBA" (A/B testing)
    - Impact of new paid options on revenue
    - Temporal tracking of evolution

    Phase 2: IMPACT MODELING
    - Estimation of additional sales generated by NBA
    - ROI of the backend team = revenue generated / team costs
    - Sensitivity analysis: "if we reduce the team, impact on revenue"

    Phase 3: MONTHLY REPORTING
    - Monthly report for management (SAFe level)
    - Real-time dashboards
    - Executive summary for decision makers
    - Tracking of key KPIs

    📊 RESULTS

    ✓ Management convinced of the value
    ✓ Result: The team received an additional developer to accelerate the addition of new options
    ✓ Monthly report requested cross-plane

    đŸ’Œ KEY LEARNINGS

    1. Data without storytelling = nothing. Narration is crucial.
    2. Well-thought-out hypotheses = powerful tools for justifying budgets
    3. Monthly reporting = creating an artifact that is consulted regularly = success
    4. In large organizations, understanding SAFe/Agile = facilitated communication

    đŸ› ïž TECHNOLOGIES

    A/B Testing Analysis | Python | Excel/PowerPoint | Hypothetical Modeling

    👉 IF YOU HAVE A SIMILAR NEED...
    Justify team budgets? Create strategic reporting?
    Contact me to discuss.
    A/B Testing Microsoft Excel SAFe Python Reporting & Analysis

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Education

  • Engineering Degree
    Polytech Nice Sophia, Valbonne, FR
    2016
    Gestion des risques, hydrogéologie, gestion de projet, Python, SQL, macros VBA
  • MSc EuroAquae
    Newcastle University, Newcastle, UK
    2015
    Impacts du changement climatique sur les milieux cĂŽtiers, VBA, anticipation des crues et inondations, gestion de bassin versant

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