Malick Diome

Data Engineer certified in Microsoft Fabric, with over 4 years of experience designing and industrializing Big Data and ETL/ELT pipelines, primarily with Apache NiFi. Specialized in cloud (AWS) environments, DataOps/DevOps practices and data modelling, with recent expertise in the Microsoft Fabric ecosystem (Lakehouse, Data Warehouse, Data Factory, Spark Notebooks), applied on the Big Data platform of the DGID project. Contributed to high-volume, strategic projects: tax fraud detection, mission-critical telecom platforms and data visualization.

Data Engineer / Apache NiFi Consultant • Agile Scrum • Team of 7 - ATOS — Orange Burkina Faso Mission

(2025-08)

Migration of the KPSA provisioning platform to Apache NiFi (Kubernetes) — a mission-critical subscriber management platform (HSS / ECMS).

• Designed a robust, scalable and secure NiFi architecture for provisioning flow management
• Led the controlled migration of existing flows to NiFi with zero service interruption
• Redesigned critical flows (contract activation, suspension, resumption) with a resilience and rollback approach
• Developed Groovy and Java scripts for advanced transformations, version-controlled in Git
• Standardized NiFi best practices: versioning, naming conventions and flow governance
• Technical environment: Apache NiFi FP 3.9, Java, Groovy, Kubernetes, GitLab, Jira, Postman, SOAP/HTTP, HLR, ECMS

Data Engineer — Big Data Platform • Agile Scrum • Team of 10 - ATOS — DGID (Tax and Property Directorate, Senegal)

(2025-06)

Deployment of a Big Data platform on Kubernetes for tax fraud detection.

• Designed and deployed a containerized Big Data platform on Kubernetes
• Developed multi-source Apache NiFi ETL pipelines (APIs, databases, files)
• Integrated Python/Pandas scripts for data cleansing, transformation and enrichment
• Prepared datasets for fraud detection and visualization via Apache Superset and Power BI
• Applied Microsoft Fabric (Lakehouse, Data Warehouse, Data Factory, Dataflow Gen2, Spark Notebooks, Power BI) for data ingestion, modelling and analysis on the project
• Technical environment: Apache NiFi, Kubernetes, Spark, MinIO, PostgreSQL, Python, Scala, Airflow, Apache Superset, Power BI, Microsoft Fabric (Lakehouse, Data Warehouse, Data Factory, Dataflow Gen2, Spark Notebooks)

Data Engineer — Apache NiFi • Agile Scrum • Team of 15 - La Poste Group, France (Nantes) - Nantes, France

(2023-09 - 2024-11)

ÉLAN project — overhaul of La Poste's logistics system, migrating from BAAN/GDS to SAP through a NiFi integration platform.

• Performed functional and technical requirements analysis and studied BAAN data models
• Designed and implemented BAAN ↔ SAP integration flows (InvokeHTTP, TransformXML/XSLT, JoltTransformJSON)
• Implemented dynamic flow routing (RouteOnAttribute) and version management via NiFi Registry
• Automated data storage and archiving on Amazon S3
• Built monitoring and performance KPIs with Grafana and Power BI
• Technical environment: Apache NiFi, AWS S3, Kafka, XSLT, Grafana, Power BI, Cloudera, Postman, Python, C++

Big Data Engineer / Reporting Developer • Agile Scrum • Team of 15 - ATOS — IoT Decision-Support Solution (Smart Agriculture)

(2023-02 - 2023-09)

Design of an IoT decision-support solution for connected agriculture (Raspberry Pi sensors + AWS).

• Configured MiniNiFi on Raspberry Pi for sensor data collection
• Built automated processing with AWS Glue, with storage on S3 and Redshift
• Created Power BI dashboards (yields, weather, costs) and custom DAX measures
• Integrated predictive models and ML algorithms into the data flow
• Technical environment: NiFi, AWS (Glue, Redshift, S3), Power BI, Python, Spark, SQL Server, Grafana, C++

Freelance Data Engineer • Scrum • Team of 9 - PCCI — Intelligent Call Center

(2021-02 - 2021-10)

Setup of an intelligent call center based on Amazon Connect with customer sentiment analysis.

• Configured AWS EC2 instances, IAM, and the Amazon Connect service integration
• Designed ETL pipelines with AWS Glue for real-time analysis of customer interactions
• Integrated AI models for sentiment detection and automatic call categorization
• Technical environment: AWS (EC2, Glue, Redshift, Connect), Power BI, Python, Apache NiFi, Pandas

Master's Degree (M2) - Information Systems Engineering - ESMT (2021 - 2022)

Master's Degree (M1) - Information Systems Engineering - ESMT (2020 - 2021)

Bachelor's Degree - Computer Engineering (2020)