Freelancer profile translated to English.
Back to original languageAbout Lorena
🤝 Lorena in a nutshell:
🌟 I am passionate about data engineering. I have 4.5 years of experience and have worked with clients in various sectors.
🚀 I help companies design, migrate, optimize, and automate their data pipelines. I also contribute to identifying the best technical and architectural choices and to knowledge transfer.
đź“š I create content in Data Engineering and support Data Engineers in their technical growth. I explain highly technical concepts in a simple and practical way.
⚡️ What I do:
âś… Data pipeline implementation (ETL/ELT)
âś… DataOps
🛠️ Tools & technologies:
Cloud & Data Platforms: AWS (Glue, ECR, Lambda, EC2, Athena, S3, Data Catalog,...)
Data Engineering: PySpark, Python, Databricks, dbt
Infrastructure: IaC, Terraform, CloudFormation
CI/CD: Docker, ECR, Github Actions, Git, Gitlab CI/CD
Orchestration: Airflow, MWAA
đź’ˇ Why work with me?
- I am passionate about Data Engineering
- I am a very good communicator, I can convey highly technical concepts to technical or non-technical audiences
- I can provide customized and effective solutions
- I have experience in consulting
- I am business-oriented, and I understand and interpret client needs very well.
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 10km)
Experience
- DecathlonDATA ENGINEER CONSULTANTMay 2023 - November 2024 (1 year and 6 months)Paris, France
Context:
- Strategic mission led by Decathlon central: complete migration of 50+ critical pipelines from Redshift to Databricks and decommissioning of legacy infrastructure.
- Challenges: reduction of infrastructure costs, performance improvement, and unification on the group's Databricks stack.
Tasks performed:
- Implementation of data pipelines in the existing Lakehouse architecture (Bronze/Silver/Gold medallion)
- Analysis of existing mapping: audit of 50+ Redshift pipelines, identification of dependencies.
- Data modeling in the Gold zone: creation of optimized dimensions according to the Kimball standard adapted for retail.
- Migration and adaptation of 50+ legacy pipelines to the different Delta Lake layers with dbt or pyspark depending on complexity
- Refactoring of complex business logic
- Implementation of advanced Spark optimization techniques: Z-ordering, broadcast joins
- Implementation of CI/CD pipelines with GitHub Actions: automated tests, multi-environment deployment (dev/preprod/prod)
- Migration of orchestration based on cron tasks/AWS schedulers to MWAA.
- Implementation of data quality tests with dbt tests and custom PySpark controls on Databricks.
- Training of local teams on new technologies and best practices.
Technical Stack: Databricks, Delta Lake, dbt Core, PySpark, Airflow (MWAA), GitHubActions, s3, AWS ECR - MichelinDATA ENGINEER CONSULTANTDecember 2024 - April 2025 (4 months)
Context:
- Specialized consulting mission to support Michelin teams (France + India) in adopting dbt and Databricks.
- Leveraging expertise gained during the Decathlon mission to transfer Modern Data Stack best practices.
- Objective: upskilling teams and exploring technical possibilities through PoCs.
Tasks performed:
- • Supporting teams on advanced dbt usage (modeling, testing, documentation)
- Responding to specific technical needs and resolving complex blockers
- Developing PoCs to demonstrate advanced dbt capabilities
- Technical benchmark and comparison of orchestration solutions (Airflow vs Dagster vs Prefect) through practical PoCs
- Implementation of advanced patterns: lineage, automated data quality checks (Great Expectations, Elementary)
Technical Stack: dbt core (advanced Jinja templating, custom macros), Airflow,Dagster, Prefect, Python, SQL, Great Expectations, Elementary - PSADATA ENGINEER CONSULTANTNovember 2022 - April 2023 (5 months)
Context:
- Processing of 1TB+ of financial data (Belgium and Netherlands) with high availability and performance requirements
Tasks performed:
- Development and optimization of Spark pipelines (Pyspark) for processing large volumes of financial data
- Implementation of new data pipelines according to evolving business needs
- Preventive and corrective maintenance of the Datalake: performance monitoring, troubleshooting.
Technical Stack: Apache Spark, SQL, DBeaver, Python
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Education
- Engineering DegreeESIGELEC2020Data Ingenierie, DevOps, Architecture et services cloud, Big Data
Certifications
- Databricks Certified Data Engineer AssociateDatabricks2023
- Databricks Certified Associate Developer for Apache SparkDatabricks2025