Paula Fonte · Freelance data engineering & analytics

Make your data workflows clearer, more reliable, and easier to use.

I help teams structure pipelines, analytical models, and automation layers without adding unnecessary complexity.

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  • Paris · Remote · On-site
  • Applied mathematics + data engineering
  • GCP, BigQuery, Python, SQL

Data cycle

Support where the data chain gets blocked.

A structure built around three key moments: connect the sources, clarify the models, automate the usage.

Collect

Connect and stabilize data flows

When data is scattered or handled manually, I build a more robust layer to centralize it and support downstream use.

  • APIs, files, ERP, IoT
  • ETL and orchestration
  • GCP / BigQuery

Structure

Clarify analytical models

I turn hard-to-use tables into readable, consistent models that make reporting easier to trust.

  • Advanced SQL
  • Explicit business rules
  • More reliable dashboards

Activate

Automate recurring work

I automate updates, synchronizations, and checks to reduce manual work and make operations smoother.

  • Python
  • Cloud Run / scripts
  • Tracked workflows

Selected work

Concrete examples showing how I structure a problem, secure the flow, and prepare data that teams can actually use.

Analytics modeling

Structure a clearer analytical layer

When analysis depends on scattered queries, it becomes difficult to produce reliable and comparable indicators.

A clearer analytical foundation that makes business calculations more reliable and recurring analysis faster.

  • SQL
  • BigQuery
  • Analytics
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Data pipelines

Stabilize an end-to-end pipeline

External data has to be collected, transformed, and loaded automatically without multiplying manual handling.

A more robust and traceable flow, ready to support reporting or downstream operational use.

  • GCP
  • BigQuery
  • Airflow
  • ETL
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Predictive analysis

Connect mathematical rigor with business use

Some teams need to go beyond reporting to anticipate demand, detect anomalies, or improve operational steering.

Predictive analysis designed to support decisions without losing readability or rigor.

  • Python
  • Scikit-learn
  • Data Science
  • Mathematics
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Approach

A working style designed for complex systems.

The value is not only in the tool that gets delivered, but in the clarity of the system that remains after the mission.

Break the problem down

I first clarify flows, dependencies, and business usage before adding technical layers.

Make data readable

A useful pipeline should be reliable, but also understandable for the team that inherits it.

Design for longevity

I favor focused, documented, and maintainable solutions.

Engagement

Three steps to move cleanly.

Short or long missions follow the same logic: understand, design, deliver clearly.

1

Map

Understand the sources, the friction points, and the business goals.

2

Design

Define a simple structure for flows, models, and automation.

3

Deliver

Provide a usable, documented base that the team can take over easily.

Contact

Need a clearer data foundation?

I work with teams that need to stabilize a pipeline, structure reporting, or automate a critical workflow.

  • Freelance missions from one month
  • Paris, on-site or remote in France
  • French, English, and Spanish
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