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Mohamed DaoudiMD

Mohamed Daoudi

Data Engineer

€500/day
Paris, FR
3-7 years
Python
Airflow
SQL
Google Cloud
BigQuery

Average response time: 1 hour

Freelancer profile translated to English.
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About Mohamed

Data Engineer specialized in the Google Cloud Platform ecosystem, I support companies in building and industrializing their data pipelines, from ingestion to business delivery.
For nearly 3 years at Crédit Agricole Leasing & Factoring, I have been designing GCP pipelines (BigQuery, Cloud Storage, Cloud Composer / Airflow) for finance, risk, and marketing teams. I am involved in data ingestion and modeling, as well as SQL optimization, quality, orchestration, and delivery via Power BI and Streamlit.
In parallel, I contribute to the KESK'IA program on a territorial diagnosis POC (Silver Readiness Index) combining multi-source public data (INSEE, DREES, FINESS, CAF…) and migration to GCP.
What I can bring you:
• Design and industrialize your data pipelines on GCP
• Structure your datasets (modeling, partitioning, data catalog)
• Automate your processes with Airflow / Cloud Composer
• Optimize your costly SQL queries
• Implement quality controls and alerting
• Deliver your data (Power BI, Streamlit, documentation)
Main stack: Python · SQL · GCP (BigQuery, Cloud Storage, Cloud Composer) · Airflow · PostgreSQL · Docker · Git / CI/CD
Available for on-site (Paris / IDF) or hybrid assignments.

  • French

    Native or bilingual

  • English

    Conversational

Can work on-site
Paris (up to 50km)

Experience

  • Programme KESK'IA
    Data Engineer
    February 2026 - Today (6 months)
    Paris, France
    Participation in the KESK’IA POC, a territorial diagnosis project for Crédit Agricole. The objective: to build a Silver Readiness Index (SRI) to assess demographic pressure, healthcare provision, accessibility, and housing vulnerability at the municipal level.

    🔹 Data Ingestion:
    • Development of Python scripts for data extraction via multi-source REST API calls: INSEE (population, income Filosofi, census), DREES (APA, dependency), FINESS (health facilities, nursing homes, SSIAD), BPE (local facilities), CAF (RSA beneficiaries), DGCL (local finances)
    • Population of 76 KPIs covering 13 categories: dependency insurance, housing adaptation loans, retirement savings, home support, demographic pressure, healthcare provision, accessibility, local finances, etc.

    🔹 Modeling & Storage:
    • Design of a data catalog referencing all sources, metadata, granularities (municipality, IRIS, department), and transformation rules
    • Creation and structuring of relational tables in PostgreSQL for storing territorial data
    • Migration of the entire dataset to GCP (BigQuery for analytics, Cloud Storage for raw storage) for scaling purposes

    🔹 Quality Control:
    • Implementation of a data reliability scoring system (grades A to D based on the number of proxies used)
    • Results: 72% of KPIs validated OK, 27% partial, 0% missing, 100% coverage
    • 64% of KPIs in grade A (direct real data, 0 proxy)

    🔹 Composite scores produced:
    • SRI (Silver Readiness Index, 0-100): demography x 30% + healthcare x 25% + accessibility x 20% + housing x 25%
    • IP (Pressure Index), IRI (Infrastructure Readiness), Budgetary Stress

    Stack: Python, SQL, PostgreSQL, GCP (BigQuery, Cloud Storage), REST API, Pandas
    Python BigQuery Google Cloud API Database
  • Crédit Agricole Leasing & Factoring
    Data Engineer
    September 2023 - Today (2 years and 11 months)
    Montrouge, France
    Integrated into the Crédit Agricole Data team as a Data Engineer. Contribution to the design, optimization, and maintenance of data pipelines on the Google Cloud Platform ecosystem.

    🔹 Pipelines & Data Ingestion:
    • Implementation of data ingestion pipelines on GCP: storage on Cloud Storage, loading and exploitation in BigQuery
    • Structuring of datasets: table partitioning, schema definition, storage optimization
    • Automation and orchestration of data processes via Apache Airflow (Cloud Composer): scheduling, task dependencies, error management
    • Maintenance and evolution of existing pipelines: bug fixes, business rule adjustments, supervision of daily runs

    🔹 SQL & Optimization:
    • Development of advanced SQL queries for populating analytical tables used by business teams (finance, risk, marketing)
    • Performance optimization: rewriting costly queries, refactoring joins, adding upstream filters, reducing execution times

    🔹 Data Quality:
    • Implementation of consistency and quality controls: volume checks, null value detection, format validation
    • Alerting in case of anomalies detected in data flows

    🔹 Delivery & Business Support:
    • Creation of Power BI reports and dashboards from prepared data for activity monitoring
    • Development and deployment of Streamlit applications for interactive data delivery
    • Writing technical documentation (SQL queries, data flows, transformation rules)
    • Support for business teams on the use and understanding of data

    Stack: Python, SQL, GCP (BigQuery, Cloud Storage, Cloud Composer), Apache Airflow, Power BI, Streamlit, Docker, Git, CI/CD
    SQL Python Google Cloud BigQuery Airflow

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Education

  • Master of Computer Science, Data Engineer Specialization
    Paris YNOV Campus
    Master Informatique spécialité Data Engineer
  • Bachelor of Computer Science, Data Option
    Paris YNOV Campus
    Licence Informatique option data

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