Yannis Ikni

Lead Data Engineer responsible for the design, industrialization, and operation of an Azure cloud data platform (Data Lake, Databricks Lakehouse, Power BI), ensuring data quality, governance, and value creation for business steering and AI use cases.

- Design, build, and maintain scalable ETL/ELT data pipelines on Azure Data Factory from MySQL, MongoDB, and HubSpot sources to an Azure Data Lake and a Databricks Lakehouse (cloud data warehouse).

- Develop and optimize data transformation workflows and KPIs with Databricks and PySpark, improving performance, scalability, and cost efficiency.

- Define and apply data governance standards, metadata management, and data lineage; automate data quality checks (freshness, business checks, anomaly detection).

- Primary point of contact for the AI platform: support for model training and deployment pipelines and feature data for data science and business teams.

- Design and evolve Power BI data models; create business steering dashboards and technical monitoring reports (self-service analytics).

- Power BI administration and governance; migration to a new Azure tenant and to Power BI Fabric.

• Technical Data Referent: upskilling the team, coordinating external partners (Microsoft, Databricks, consultants).

Scrum Master for the Data team (Agile project management).

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Develop data applications and dashboards with Python (Dash) powered by SQL data pipelines from PostgreSQL.

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Implement CI/CD pipelines with Docker image building and orchestration on a Kubernetes cluster.

Employee experience – improvement of the internal search engine (SharePoint).

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Collect data from an Azure cloud Data Warehouse and by scraping documents.

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Build unsupervised machine learning pipelines for automatic page tagging by keywords via Databricks (feature engineering, model training).

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Develop a custom spell correction model and define KPIs to evaluate search performance.