Khaoula Lakhlifi

Scope: Implementation of a data quality control framework on a data lake and integration into a multimodal analytics platform. Mission:

• End-to-end design and deployment of an industrialized data quality framework, ensuring data reliability throughout analytical pipelines.

• Development of scalable and robust ETL/ELT pipelines for ingesting, transforming, and standardizing data from multiple sources (structured and semi-structured) in a Databricks cloud environment.

• Data modeling and transformation to support business analytics and reporting.

• Implementation of cross-cutting controls and business consistency rules, ensuring alignment of indicators across multiple datasets.

• Optimization of PySpark processing performance (partitioning, Delta format management, query optimization).

• Exposure of reusable analytical datasets via interactive dashboards for non-technical users.

• Implementation of best practices for governance, traceability, and technical documentation (Git, structured documentation).

• Close collaboration with data, product, and business teams to translate business needs into industrialized data solutions.

Technologies: Python, SQL, PySpark, Databricks, Delta Lake, Tableau, Streamlit, Plotly, Git, Jira.

Scope: Construction of a decision-making system for monitoring financial indicators and optimizing client portfolio performance. Mission:

• Design and development of industrialized ETL/ELT pipelines ensuring reliability, traceability, and performance of data flows.

• Orchestration and scheduling of workflows with Apache Airflow to automate ingestion and transformations.

• Collection, preparation, and integration of multi-source data (SQL databases, files, internal systems).

• Structuring transformations based on principles similar to DBT (models, quality tests, documentation).

• Manipulation and optimization of data on cloud data warehouses (BigQuery, Snowflake).

• Optimization of SQL queries and improvement of pipeline performance.

• Implementation of data quality controls and anomaly detection in data flows.

• Creation of Power BI dashboards for monitoring business indicators.

• Collaboration with business teams to translate needs into usable datasets.

Technologies: Python, SQL, Apache Airflow, Google Cloud Platform, BigQuery, Snowflake, Power BI, Git, Jira.

• Preprocessing and validation of multi-source data (IoT, weather, soil) with Python, Pandas, and NumPy.

• Design of an automated data preparation, transformation, and analysis pipeline for agricultural data exploitation.

• Structuring data transformations based on principles similar to DBT (dataset modeling, layered organization, testing logic, and documentation).

• Manipulation and storage of analytical data on cloud environments (Snowflake, Azure).

• Development and training of a machine learning model for predicting crop water needs, based on climatic conditions and soil characteristics.

• Development of a computer vision model with PyTorch for automatic detection of tree diseases from images.

• Evaluation and improvement of model performance through validation and hyperparameter optimization.

• Design and deployment of interactive dashboards for visualizing indicators and predictions with Power BI.

• Participation in the analysis of business needs and support for teams in data exploitation for decision-making.

• Optimization of cloud architecture and environment management via Terraform.

• Collaboration with business experts and technical teams to improve AI models and data pipelines.

Technologies: Python, Pandas, NumPy, SQL, Microsoft Azure, Terraform, Qlik, Git, PyTorch.