Achref ZAGH

Within the Data & Analytics department, my role focused on driving data engineering initiatives across subdomains including Digital Manufacturing Acceleration (DMA), Supply Chain (SPC), and Cycle and Procurement (CNP) at Danone. Using the One Source platform, I applied advanced data engineering techniques to develop data pipelines, ETL processes, and real-time analytics for over 10 projects, including:

Project team: 1 PM, 1 PO, 1 Data Engineer, 1 Data Architect,1 Data Analyst, 2 IOT Engineers Objective: Cortex is the cornerstone of DMA (Digital Manufacturing Acceleration) program, aiming to design and implement data-driven factories concept of the industry 4.0 by enabling them to connect their various systems and leverage diverse data sources in real-time: Tasks: • Participation in POCs around Danone's Cloud platform. • Preparing Prerequisites for Authentications and Connecting Solace to Event Hub. • Understand the Unity Catalog architecture and integration requirements. • Define all required configurations, including external locations, schemas, and access permissions. • Design and develop the integration between the Medallion layers and Unity Catalog. • Determine the folder's structure, architecture, and coding standards for the template. • Develop the base structure of the microservice template, including directories, files, and configurations. • Design the Bronze, Silver and gold layers and define their data model. • Develop microservices to consume data from Event Hubs, process it, and write it to the lakehouse architecture. • Implement error handling, retry mechanism for failed data transfers and data quality checks. • Use Grafana to build real-time reports and dashboards alongside Power BI analytics. Technologies: Azure Databricks, Snowflake, Apache Spark, Azure Data Factory, DBT, ADLS Gen2, Azure Key Vault, Azure EventHubs, Azure Stream Analytics, Python, SQL, Git, Confluence.

Project team: 1 PM, PO, 2 Data Engineers, 1 Data Architect,1 Data Analyst,1 intern. Objective: Deliver high-quality manufacturing data as key performance indicators (KPIs) for Manufacturing, Quality, Finance, HR, Nature, and Safety. Present KPIs through Power BI dashboards using data from the Common Data Set Warehouse (CDS). Tasks: • Create and manage multiple Azure Data Factory (ADF) pipelines for various system sources. • Implement data transformations for different data types using Databricks • Create taskflows using Informatica for data integration and orchestrate job scheduling with Stonebranch • Extract, load, transform data from different data sources (Sphera, Performance, Greentrack, Magnitude) using Informatica IICS and Azure Data Factory ADF. • Create external stages and stored procedures to load XML files from ADLS to Snowflake. • Design and implement dimensional data models to support business analytics and reporting in Snowflake. • Orchestrate and create all the workflows within Stonebranch. • Collaborate closely with Data Analysts to provide data insights for Power BI reporting. • Conduct code reviews and contribute to Git pull requests for code quality assurance. • Mentor junior Data Engineers and interns in task resolution and skill development. • Work in an Agile Scrum methodology for project delivery, utilizing tools like Jira and Confluence to document each project. Technologies: Azure Databricks, Snowflake, Apache Spark, Informatica IICS, Azure Data Factory, ADLS Gen2, Azure Key Vault, Stonebranch, Python, SQL, Postman, Git, Confluence.

As a junior member of the Data Unit Department, I played a pivotal role in supporting innovative projects aimed at modernizing data management and enhancing security within the organization. Collaborating closely with experienced teams, they contributed to advancements in technology and methodology to optimize operations across various domains:

DataFlow+ was about making big changes to how we handle data. We worked on making data move better, keeping our database safe, and making easy-to-understand charts and graphs to help everyone work together better. Project team: 1 PM, 3 Data Engineers, 1 Data Architect,1 Data Analyst Tasks: • Design, develop and automate data flows (data processing, transformation and integration) using Databricks. • Design and manage a Snowflake database. • Technical design in collaboration with the program's architects. • Work on POCs to facilitate the choice of technical design. • Realization of a proof-of-concept POC on Batch / Streaming type data. • Creation of a Dashboard visualization system using Power BI. Technologies: Databricks, Microsoft AZURE, Snowflake, PowerBI, Python, Pandas, Scikit-learn, Tensorflow, SQL

With SecureSense, we aimed to use big data to make things more secure. We looked at info from firewalls and other sources to find and fix problems. We also made tools to help us understand the data better and keep everything safe. Project team: 1 PM,1 Big Data Engineer, 1 Data Architect,1 web developer. Tasks: • Collect structured and unstructured data from log files using Logstash. • Parsing and integrating firewall log files in Elasticsearch using Logstash. • Creating a Visualization Dashboard system using Kibana. • Analyzing and correlating data from different sources using Elasticsearch. • Implementing data enrichment pipelines in Logstash. • Configuring security and access control in Elasticsearch. • Creating custom visualizations and dashboards in Kibana to monitor security metrics. • Presenting data in charts, graphs, tables, designing and developing relational databases for collecting data and in some organizations it also includes building or designing data input or data collection screens. Technologies: Python, Elasticsearch, Kibana, Maven, Git , Java, Hadoop, Logstash, grokdebugger,Slack