Databricks & Lakehouse
Lakehouse architecture on AWS with Databricks and S3. Delta Lake tables organised in Bronze, Silver and Gold layers. Notebooks, ingestion and transformation workloads industrialised for production.
Available immediately · Paris, France
Senior Data Engineer & Cloud Architect
I design & build data platforms that scale.
Databricks Lakehouse architect and AWS Certified Solutions Architect, based in Paris. I turn raw events into governed, reliable, analytics-ready data on Databricks and AWS.
- PlatformDatabricks · Delta Lake · Unity Catalog
- CloudAWS Certified Solutions Architect – Associate
- EngineApache Spark · PySpark · Python · SQL
- Databricks
- Apache Spark
- PySpark
- Delta Lake
- Unity Catalog
- AWS
- Amazon S3
- Amazon Kinesis
- AWS Lambda
- DynamoDB
- AWS Glue
- Amazon EC2
- RDS PostgreSQL
- CloudFormation
- CloudWatch
- Python
- SQL
- Power BI
- Tableau
01 — Profile
Engineer by training, architect by practice.
I'm a Data, AI & Cloud engineer with a solid track record designing data pipelines, AWS cloud architectures and analytical solutions. My toolkit centres on Python, SQL, PySpark, Databricks and the Lakehouse architecture.
Curious, rigorous and results-driven, I build solutions that are performant, secure and designed to evolve with the business. From a highway operator's field data to a real-time event platform and a governed Lakehouse, the constant is the same: data people can trust.
- Based in
- Paris, France
- Availability
- Immediate
- Certified
- AWS Solutions Architect – Associate
- Education
- MSc Data Analytics, ENSAE Paris
MSc Communication Systems & Embedded Electronics, ENSA Tanger - Languages
- Arabic (native) · French (native) · English (professional)
- Nationality
- Moroccan / French
- Beyond work
- GenAI research & LLMs · Blockchain · Climbing & fitness · Travel
02 — Experience
From pipelines to platforms.
Four roles, one trajectory: each platform more distributed, more governed and more real-time than the last.
-
Data Architect / Databricks Data Engineer
- Co-designed a Lakehouse architecture on AWS with Databricks and Amazon S3.
- Built ingestion and transformation pipelines in Python and PySpark.
- Implemented Delta Lake with a Bronze / Silver / Gold medallion architecture.
- Set up data governance in Unity Catalog: catalogs, schemas, tables and access control.
- Optimised and industrialised the data engineering workloads.
- Exposed curated data to BI and analytics tools.
-
AWS Data Architect / Senior Data Engineer
- Designed a real-time data pipeline built on Amazon Kinesis and AWS Lambda.
- Developed the event ingestion and transformation logic in Python.
- Served real-time data from DynamoDB, with long-term archiving in Amazon S3.
- Set up monitoring and alerting with Amazon CloudWatch.
- Contributed to architecture decisions on scalability and stream processing.
- Improved operational monitoring and anomaly detection.
-
AWS Cloud Architect / Data Engineer
- Co-designed a distributed Big Data processing architecture on Amazon EC2.
- Developed PySpark ETL jobs over large data volumes.
- Ingested CSV and JSON files from Amazon S3.
- Optimised Spark jobs through partitioning and distributed transformations.
- Loaded the transformed data into Amazon RDS for PostgreSQL.
- Automated deployment and supervision with CloudFormation and CloudWatch.
-
Data Engineer
- Motorway fencing — audit, cleaning, analysis and exploration in Python, with map-based monitoring.
- Asbestos technical file — cleaning, analysis, optimisation and exploration with Python and Power Query.
- Tracking indicators — data collection, cleaning and predictive analysis in Python.
- Rest-area indicators — built an end-to-end data pipeline (collection → cleaning → reporting) to monitor concession-holders and facilities, with KPIs delivered in Power BI.
03 — Blueprints
Platforms I've built, drawn as I built them.
Three architectures from three real engagements. Hover or tap any component to see what it does and why it's there.
Interactive — hover, tap or use the keyboardScroll sideways · tap a component
Component
Select a component
Every node on this diagram maps to work described in my experience. Hover or focus one to read the role it plays.
Key decisions
04 — Expertise
A modern data stack, end to end.
The tools I use to take data from source to decision, and the depth I bring to each.
Apache Spark & PySpark
Distributed processing and transformation at volume. Partitioning strategies and distributed transformations tuned to the shape of the data.
Data Governance
Unity Catalog as the control plane: catalogs, schemas, tables and access modelled explicitly so the right people see the right data.
AWS Cloud Architecture
Certified Solutions Architect – Associate. I design data platforms with the AWS building blocks that fit the workload: object storage, streaming, serverless compute, managed databases and infrastructure as code.
- S3
- Kinesis
- Lambda
- DynamoDB
- Glue
- EC2
- RDS PostgreSQL
- CloudFormation
- CloudWatch
Python & Advanced SQL
ETL development, complex queries, query optimisation and data modelling. The two languages behind every pipeline I've shipped.
Databases
Relational and NoSQL stores chosen for the access pattern: PostgreSQL and SQL Server for analytics, DynamoDB for real-time reads.
BI & Delivery
Data is only useful once it's seen. Dashboards in Power BI and Tableau, delivered in Agile / Scrum teams with Git and Jira.
05 — How I build
Principles that survive production.
-
01
Layer by trust.
Bronze keeps the raw truth, Silver cleans and conforms, Gold serves the business. Every table carries a known level of confidence, and every problem can be replayed from source.
-
02
Govern from day one.
Catalogs, schemas, tables and access are modelled in Unity Catalog before the first dashboard ships, not after the first incident.
-
03
Automate the platform.
Infrastructure described in CloudFormation, deployments that repeat identically, environments that can be rebuilt rather than repaired.
-
04
Observe everything.
CloudWatch metrics, alerts and anomaly detection are part of the pipeline design, so an issue is seen by the platform before it is felt by the business.
06 — Credentials
Certified on the cloud, trained on the platform.
AWS Certified Solutions Architect – Associate
Amazon Web Services
Verify on CredlyDatabricks Platform Architecture Training
Databricks
Master's Degree in Data Analytics
ENSAE Paris — specialisation in Data Science and Data Analytics
Master's Degree in Communication Systems & Embedded Electronics
ENSA Tanger — advanced coursework in communication systems and embedded electronics
07 — Contact
Let's build something that lasts.
Open to Data Engineering, Databricks and AWS platform roles and missions. Based in Paris, available immediately.