Alexis Metwalli
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Alexis Metwalli

Data Platform Engineer

I help data teams assess, modernize, and industrialize their data platforms—from business requirements to production infrastructure.

I’m passionate about building mature, efficient ecosystems where data engineers can focus on delivering value—not rebuilding the same foundations.

  • Databricks
  • Apache Spark
  • dbt
  • AWS / Azure
  • Terraform
Discuss your data platform ↗View my projects ↓
Portrait of Alexis Metwalli
Paris, FranceWork in hybrid / remote
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01 / ABOUT

From the business question to the running platform.

My strength is versatility across the delivery chain. I can understand the client’s need, turn it into the right data model, implement the transformations, and provision the compute and storage needed to run it reliably.

This end-to-end view lets me make technical decisions with both the business outcome and the operating reality in mind.

  1. 01
    Understand

    Clarify the client need and the decision the data must support.

  2. 02
    Model

    Design data models and contracts that reflect the business.

  3. 03
    Transform

    Implement tested, maintainable pipelines and transformations.

  4. 04
    Operate

    Provision compute, storage, deployment, and observability.

02 / EXPERTISE

What I can take ownership of, and how I deliver it.

Expertise

01

Data Engineering

Turn business requirements into tested data models and production pipelines that remain understandable as they grow.

What I can do

  • Design batch and streaming ingestion pipelines
  • Model business domains and transformation layers
  • Migrate and refactor legacy SQL logic
  • Diagnose Spark bottlenecks, skew, and slow models
  • Build technical and business data-quality controls

How I deliver it

  • Apache Spark
  • Java / Scala
  • Python
  • SQL
  • dbt
  • Data contracts
  • DuckDB
  • Trino
02

Data Platforms

Build the shared ecosystem around pipelines so data engineers can deliver consistently instead of solving the same platform problems repeatedly.

What I can do

  • Assess architecture, codebases, and engineering practices
  • Design lakehouse and Medallion architectures
  • Define reusable patterns for data products and onboarding
  • Introduce orchestration, lineage, governance, and observability
  • Plan platform modernization and migration strategies

How I deliver it

  • Databricks
  • Delta Lake
  • Apache Iceberg
  • Airflow
  • Dagster
  • OpenMetadata
  • Apache Superset
  • Apache Polaris
  • Unity Catalog
03

Cloud & Infrastructure

Provision the compute, storage, and deployment foundations required to move data workloads from prototype to reliable production.

What I can do

  • Design cloud infrastructure for data workloads
  • Industrialize proofs of concept for production
  • Build repeatable Infrastructure as Code modules
  • Set up CI/CD, deployment, and environment strategies
  • Improve operational reliability and handover

How I deliver it

  • AWS
  • Microsoft Azure
  • Terraform
  • Docker
  • Kubernetes
  • Helm
  • GitHub Actions
  • GitLab CI
  • Grafana

03 / SELECTED WORK

Platforms taken over, modernized, and made reliable.

A few selected engagements, focused on problems solved rather than a wall of logos.

01

Azure Databricks · enterprise data platform

Sonepar

Ownership and evolution of a legacy platform supporting a unified data layer, acting as technical lead.

  • Took ownership within one month of a platform built over five years
  • Integrated 100+ domain-oriented data products
  • Designed technical and business data-quality frameworks
  • Contributed to the migration strategy toward Microsoft Fabric
50 → 10 min
Critical Spark pipeline
100+
Domain data products integrated
1 month
To take ownership of a 5-year platform
  • Azure Databricks
  • Spark
  • Python
  • SQL
  • Microsoft Fabric
02

Talend → Databricks infrastructure · Talend SQL → dbt

Audemars Piguet

Migrated the infrastructure and data flows to a Databricks lakehouse while rebuilding legacy Talend jobs as maintainable dbt models running on Databricks.

  • Migrated platform infrastructure and data flows from Talend to Databricks
  • Reverse-engineered undocumented Talend SQL and rebuilt the job with dbt Spark SQL on Databricks
  • Reduced its runtime from 40 minutes to 2 minutes by refactoring a long query into structured CTEs
  • Brought the migrated job in line with comparable models that completed in 1–2 minutes
  • Preserved exact business outputs while introducing a Bronze / Silver / Gold Medallion architecture
40 → 2 min
Legacy job runtime
Talend → dbt
Job and SQL logic migration
Talend → Databricks
Infrastructure and data migration
  • Databricks
  • dbt
  • Spark SQL
  • Fivetran
  • Medallion
03

AWS ETL industrialization

Safran

Transformed a pandas proof of concept processing a single CSV into an industrialized ETL running on AWS Glue and Apache Spark—all within four months.

  • Re-engineered a single-file pandas prototype for distributed processing with AWS Glue and Apache Spark
  • Built production-ready ETL workflows and transformations with Python and PySpark
  • Provisioned repeatable cloud infrastructure with Terraform
  • Added the orchestration, quality controls, and operational foundations required for production
4 months
POC-to-production delivery
pandas → Spark
Prototype to distributed ETL
AWS Glue
Industrialized production runtime
  • AWS Glue
  • PySpark
  • Python
  • Terraform

04 / OPEN SOURCE

Building a better engineering environment.

My platform-engineering ideas made tangible: a mature ecosystem for delivery, deployment, ingestion, and reliability.

01 / Lakehouse platform

OpenLakeForge

An open-source lakehouse platform for small data teams: self-hosted, modular, and deployable locally, on AWS, or on Azure.

  • Reusable platform primitives for orchestration, observability, governance, and metadata management.
  • Terraform and Helm workflows designed to standardize deployment across environments.
  • An infrastructure-first approach built around operational clarity for small data teams.

$ olf deploy --provider local

  • Iceberg
  • Trino
  • dbt
  • Dagster
  • Terraform
  • Helm
View on GitHub (opens in a new tab)
02 / Data contract runtime

Floe

A Rust and Polars-powered data contract runtime that validates data before it enters the trusted layer.

  • Schema enforcement and data contracts before data reaches the trusted layer.
  • A lightweight, validation-first approach to reliable file ingestion and onboarding.
  • Designed for interoperability with modern lakehouse ecosystems.

$ floe run -c config.yml

  • Rust
  • Polars
  • Arrow
  • Data Contracts
View on GitHub (opens in a new tab)
CONTRIBUTIONS

05 / RECOMMENDATIONS

What collaborators say.

Feedback from people who worked with me on complex data platforms and client engagements.

01LinkedIn · September 2026

I had the pleasure of working with Alexis as part of the Data Lake team on the Sonepar project at Onepoint. He is an experienced Data Engineer, passionate about DevOps and always keeping up with new technologies.

Alexis is highly skilled, curious, and learns extremely quickly. Above all, he is a real driving force within a team: he identifies opportunities for improvement, proposes solutions, and never hesitates to tackle technical debt.

I particularly valued his ability to turn an idea into a concrete solution, from design through to production, with a strong focus on delivery and results.

I recommend him to any team looking for an autonomous, committed, and impact-driven technical professional.

02LinkedIn · September 2026

I had the pleasure of working with Alexis on my team at Safran, where he served as a Data Platform Engineer on a complex legacy application.

He joined during a challenging team transition and quickly gained an understanding of the environment, business needs, and technical constraints. Over time, he took on greater responsibility for technical decisions, always with a pragmatic, project-focused approach. His ability to challenge existing practices and find concrete solutions helped move the project forward.

I particularly appreciated his reliability, commitment, and team spirit. He consistently went beyond his responsibilities, contributed to the team dynamic, and earned the trust of both colleagues and customers.

Alexis also demonstrates strong technical curiosity through his analyses and research. His ability to experiment, learn, and make complex topics accessible is a real asset.

I highly recommend Alexis for his technical expertise, adaptability, commitment, and constructive approach to complex environments.

View all recommendations on LinkedIn (opens in a new tab)

06 / EXPERIENCE

Professional experience

One consistent thread: making data systems easier to understand, deploy, and operate.

  1. Arkose

    Data Platform Engineer

    Assessed the existing data platform and defined prioritized recommendations for its evolution.

    Consulting firmOnepoint

  2. Audemars Piguet

    Data Engineer

    Migrated legacy Talend workloads to dbt and Databricks while preserving their business outputs.

    Consulting firmOnepoint

  3. Sonepar

    Data Engineer / Databricks

    Led a large Azure Databricks platform and improved performance, quality, and data-product integration.

    Consulting firmOnepoint

  4. Safran

    AWS Data Engineer & DevOps

    Industrialized a pandas proof of concept into a production AWS Glue and Spark ETL in four months.

    Consulting firmOnepoint

  5. Brut

    Big Data Engineer

    Prepared a GCP-to-AWS migration and built a reusable dbt and Dagster ETL framework.

    Consulting firmDevoteam A Cloud

  6. Safran

    Data Engineer

    Built backend services, ingestion workflows, and Terraform infrastructure for a maintenance platform.

    Consulting firmOnepoint

  7. Chanel

    Data Engineer Intern

    Built reusable Scala and Spark ingestion components for real-time lakehouse workloads.

    Consulting firmOnepoint

EDUCATION

Academic background

  1. INSA Lyon

    Engineering Degree / MSc in Computer Science

    Lyon, France
  2. DGIST

    Exchange Program · AI & Deep Learning

    Daegu, South Korea

CERTIFICATIONS

Certified across the platform stack

  • AWS Certified Data Engineer – Associate
  • AWS Certified Solutions Architect – Associate
  • Databricks Certified Data Engineer Professional
  • Databricks Certified Data Engineer Associate
  • Databricks Certified Associate Developer for Apache Spark
  • HashiCorp Certified: Terraform Associate
  • Microsoft Certified: Azure Fundamentals

07 / CONTACT

A data project, a migration, or a need for extra engineering capacity?

Let’s talk.

[email protected]