Ibrahima FALL

Led the complete redesign of a reimbursement request management system, including fraud detection capabilities. Designed and implemented a scalable streaming-based architecture using Google Cloud tools (Bigtable, BigQuery, Pub/Sub, Cloud Run Jobs). This solution resulted in a 90% reduction in operational costs while improving team responsiveness and system robustness. Worked closely with stakeholders to ensure alignment between technical deliverables and business needs.

In my capacity as a Senior Data Engineer at Decathlon, my journey has been defined by a commitment to knowledge sharing, transformative solutions, and significant contributions to pivotal projects. Central to my role is the active role of an educator. Concurrently, I have been tasked with imparting data engineering skills, delving into functional programming insights, and advocating best coding practices to our team. This collaborative learning approach cultivates a culture of growth, equipping our team to excel in their roles. Simultaneously, I've led the architectural design and development of a groundbreaking tool that streamlines our data integration processes. This innovation has democratised the integration of complex data pipelines, making it accessible even to non-developers. This empowerment aligns seamlessly with Decathlon's ethos of inclusivity, fostering participation across all skill levels. Subsequently, I took on the pivotal role of developing and maintaining a fidelity program. This project showcased my ability to steer initiatives of strategic importance. I led efforts to modernise and simplify the program's architecture while incorporating robust data monitoring practices. These enhancements reflect my commitment to leveraging data insights to enhance customer loyalty and engagement. My tenure as a Senior Data Engineer at Decathlon vividly reflects my passionate dedication to driving innovation, facilitating knowledge sharing, and delivering impactful contributions.

Embarking on a transformational journey as a Data Engineer, I play a pivotal role in Renault's evolution from a company rooted in legacy infrastructure to a future-ready data-driven powerhouse. Renault's rich legacy is matched only by the complexity of its existing infrastructure. In my role, I'm entrusted with the strategic initiative of migrating a comprehensive data lake from on-premise systems to the dynamic expanse of Google Cloud Platform (GCP). Guided by a vision of modernization, I meticulously plan and execute this migration, leveraging a suite of advanced tools such as BigQuery, Dataproc batch jobs, streaming pipelines via Pub/Sub, and CI/CD workflows managed with Artifact Registry and Cloud Build. This undertaking is a harmonious blend of technical prowess and strategic foresight. As I navigate the complexities of the migration, I ensure the secure, efficient transfer of intricate datasets. Beyond relocation, I'm unleashing the full potential of GCP's capabilities, enhancing scalability, agility, and performance. Enriched by this experience, I've witnessed remarkable growth. Each phase of the migration has enriched my skill set and deepened my understanding of data engineering in real-world scenarios.

In my role as a Data Engineer at Xebia, I've had the privilege of contributing to a journey marked by learning, collaboration, and growth. A key aspect of my role involves advising clients, leveraging my technical expertise to help them develop data-driven solutions aligned with their goals. This advisory role not only provides valuable insights but also sharpens my own problem-solving skills. My dedication to knowledge sharing is reflected in technical articles that cover foundational to advanced programming, including functional programming. These articles aim to enrich the industry's collective understanding, fostering a culture of continuous learning. Within our vibrant internal ecosystem, alongside a team of exceptionally talented colleagues, I offer technical guidance and support. This fosters an environment of mutual knowledge exchange and collaborative growth.

During my tenure at Galeries Lafayette, a distinguished player in the retail and fashion sector, I had the privilege of contributing to two significant projects that showcased the power of teamwork and innovation: * Data Integration into Cloud Infrastructure (GCP): I played a role in the integration of diverse data sources into a cloud environment within Google Cloud Platform (GCP). My contributions involved collaborating with a dedicated team to design and implement data pipelines that enabled efficient data flow. Through this collective effort, we improved data accessibility and laid the groundwork for more informed decision-making. * Instagram Scraping Algorithm with Machine Learning for Early Fashion Trend Detection: I had the opportunity to collaborate on a project involving an Instagram scraping algorithm enhanced by machine learning capabilities. This project aimed to identify emerging fashion trends ahead of time. Working alongside a talented team, I contributed my part to develop and fine-tune the algorithm, which eventually enabled the organization to gain valuable insights for strategic planning. These experiences taught me the importance of collaboration, adaptability, and continuous learning. My contributions were part of a larger effort, and I am grateful to have been part of a team dedicated to leveraging technology to drive positive outcomes.

Société Générale, a prominent banking institution, manages extensive volumes of critical data. To enhance data reliability and integrity, the establishment of a comprehensive framework for data control became imperative. In my capacity within the data engineering team, I am actively engaged in the development of a sophisticated data control tool. This pivotal initiative is aimed at safeguarding the bank's data integrity and reliability. My role entails not only contributing to the technical implementation but also providing advisory insights into the optimal architectural blueprint for this tool. The technologies employed in this endeavor encompass cutting-edge solutions such as Spark and Scala, which facilitate efficient data processing, alongside Kafka, Oozie, and Ansible for orchestration and automation, ensuring seamless data control and management. By participating in this initiative, I am contributing to enhancing the institution's data governance practices, reinforcing the accuracy, and reliability of its data assets while adhering to the stringent standards inherent to the banking sector. Technologies: Spark, Scala, Kafka, Oozie, Ansible.

As an experienced Data Engineer, my role centers on assisting our clients in extracting value from their data. I achieve this by crafting tailored solutions, applications, and architectures that cater to their unique needs. Additionally, I have been privileged to support our commercial team in addressing technical aspects of tender responses and facilitating consultant-client alignments. One of my key strengths lies in my ability to contribute to the strategic objectives of our clients. By architecting solutions that harness data's potential, I help inform their decision-making and drive growth. Within our commercial team, I offer technical insights that bolster our proposals, ensuring that our offerings align with our clients' expectations and underscore our technical expertise. Lastly, I value the power of knowledge sharing. Both within our organization and beyond, I contribute insights, methodologies, and trends to enhance our industry's understanding of data engineering practices. In essence, my role blends technical expertise, strategic alignment, and a dedication to fostering a culture of shared learning.

At Natixis, I played a pivotal role in addressing data consistency and automation challenges. Working within the Data Engineering team, I contributed to building a robust Datalake framework with a focus on: * Migration Tool: I developed an automated tool to seamlessly migrate data from relational databases like Oracle to NoSQL databases, streamlining processes and ensuring data integrity. Technologies: Sqoop, Java, HDFS, SQL * Universal File Integration: I architected a company-wide file integration framework capable of handling various formats, performing transformations, and integrating data into Hive Tables. Technologies: Spark, Scala, Hbase, Hive, HDFS, Kafka * Data Integrity Control: I created a program to verify data integrity, ensuring primary keys, foreign keys, and non-null fields were accurate and consistent. Technologies: Spark, Scala * Regulatory Reporting: I designed programs for aggregating, validating, and transmitting data for regulatory reporting, maintaining an audit trail. Technologies: Spark, Scala, Hbase, Hive, Phoenix * Centralized Data Source Integration: I led the creation of a program that centralized accounting, risk, and referential data, ensuring a dependable universal source. Technologies: Spark, Scala, HDFS, Hbase, Hive, Phoenix I also managed Continuous Integration and Continuous Delivery (CI/CD) processes using tools like Jenkins, XLDeploy, and ControlM. This experience honed my skills in data engineering, quality control, and compliance within the financial sector.

During my internship experience with Orange, I undertook the exploration of two pivotal areas that directly influenced the end clients, thereby contributing to the enhancement of their user experience. These areas encompassed a comprehensive study on client satisfaction, leveraging diverse datasets accumulated over several years, as well as a profound investigation into advanced data anonymization techniques, crucial for upholding client privacy. * Client Satisfaction Study: My engagement with a wealth of client-centric data, including material preferences, geographical data, responses to satisfaction surveys, and customer service interactions, empowered me to synthesize insights aimed at improving service quality. This entailed the creation of tailored Key Performance Indicators (KPIs) through the integration of data, facilitating proactive identification of potential service issues. Employing machine learning algorithms, I developed predictive models to anticipate issues and subsequently visualized trends through impactful graphical representations. Technologies: Spark, Scala, Hive, PowerBI, SQL. * Study on Anonymization Techniques: Throughout the process of data aggregation for issue comprehension, ensuring data anonymity emerged as a significant task to safeguard individual privacy. To address this, I embarked on a comprehensive study of anonymization techniques, meticulously comparing their efficacy across diverse use cases. This culminated in the application of the most suitable techniques to the dataset while preserving data utility. Technologies: Spark, Scala. My approach was supported by cutting-edge technologies like Spark and Scala, which facilitated meticulous data processing and analysis, thereby maintaining the highest standards of privacy and data security. This rewarding experience equipped me with a profound understanding of data-driven solutions within a client-focused framework, an invaluable asset in today's dynamic professional landscape.

Predictive Analysis of Digital Signature Data Authenticity In the scope of this project, our primary objective is to develop Machine Learning algorithms aimed at automating the process of verifying the authenticity of digital signatures. This approach primarily relies on the criterion of the inherent dynamics observed during the creation of these signatures. Specifically, our work revolves around the following aspects: Designing sophisticated Machine Learning algorithms capable of accurately assessing the authenticity of a signature based on its characteristic movement. Exhaustively comparing a temporal model, which relies on the dynamic aspects of signatures, with a neuromuscular model that takes into account the neuromuscular characteristics exhibited during the signing process. Thoughtfully presenting our achievements through a meticulously designed web application, enabling users to perform signature authenticity verifications in a user-friendly and intuitive manner. The technologies employed to achieve these objectives include Python, Java, and MongoDB, which have been chosen for their robustness and relevance within the context of this project. Our approach adheres to a rigorous development, validation, and results presentation methodology, with the ultimate goal of providing a reliable and high-performance solution for digital signature authentication.