Michel Hua

I offer independent consulting services, specializing in cloud data applications for mid-sized and large companies. My range of services includes: - building web, analytics, and machine learning applications - software migration - legacy code management - auditing systems - data governance - team transition - improving software delivery - exploring and developing strategies for emerging technologies, including generative AI If you need projects delivered quickly with transparent communication, I can help formalize and tackle broad IT problems. I have advanced expertise with various cloud providers and can easily adapt to international teams. Main clients: - Trustpair, - TotalEnergies, - Sanofi, - OECD, - Air Liquide Software stack: - Distributed computing: Athena, EMR, Functions App, SQL Server, PostgreSQL, OpenAI, Snowflake, Bedrock, PowerBI - Languages: Python, TypeScript - Cloud providers: AWS, Azure

Role Overview: Developed and implemented cloud data applications for large enterprises, enhancing their data capabilities and driving business insights. Key Projects: - Marketing Platforms: Designed and deployed scalable marketing platforms to optimize customer engagement. - Data Warehouses: Built robust data warehouses to centralize and streamline data management. Machine Learning Services: Developed machine learning models and services to support predictive analytics. - Distributed Analytics: Implemented distributed analytics solutions for real-time data processing and insights. Internal Contributions: - conducted comprehensive technical analyses on emerging technologies and trends. - fostered developer relations with Microsoft cloud partners, exploring new services like OpenAI and Fabric. - shared innovative ideas and best practices with international teams. - participated in advanced partner training from GCP, Azure, and Snowflake, and attended annual developer events. Main clients: - Econocom, - Catalina Marketing, - Decathlon Technology Software stack: - Distributed computing: BigQuery, Snowflake, Cassandra, Celery, Spark, Airflow, SageMaker, Redis - Web applications: FastAPI, React - Monitoring tools: PagerDuty, Datadog, New Relic - Languages: Python, JavaScript, SQL - Cloud providers: GCP, AWS, Azure

Moët Hennessy Information Services Wrote a change-data-capture pipeline from SAP databases to BigQuery. Worked on data-mesh type organization proposal. Software stack: - Distributed computing: BigQuery, Dataflow, Pub/Sub - Languages: Python, Java, SQL

Tech team Helped a growth-stage personal finance startup scale its data infrastructure using analytics: - event sourcing system - real time marketing attribution analytics - customer-ranking pipelines - discussed privacy related issues Software stack: - Distributed computing: BigQuery, Dataflow, Airflow, Pub/Sub - Languages: Node.js, Python, Java, SQL - Tools: Metabase, dbt CLI -- Data Engineering, Cloud Architecture

Adevinta France / Product & Technology Leveraged neural networks based algorithms in content classification, data extraction for European classified advertisements websites, including the French leader LeBonCoin: - transitioned a project between two European teams - migrated code related to machine learning workloads and training datasets from GCP to AWS - explored feature evolutions of the codebase Advised teams on data architecture with a broader scope to make machine learning scale within the Spanish area. Software stack: - Distributed computing: Tensorflow, Spark, Node.js - Orchestration systems: Docker, Kubernetes, Argo -- Data Engineering, Deep Learning, Software as a Service

SeLoger / Data department In a cloud data strategy context, rewrote major end-to-end machine learning applications, from extraction jobs to algorithms and microservices: - SeLoger.com: listings-recommendation model, collaborative filtering - LaCoteImmo.com: housing-value model, random forest - SeLogerPlus.com: customer-ranking model, rule-based scoring - user data collection and mail analytics platforms Optimized algorithms and cloud costs. Documented, migrated codebase and automated monitoring on legacy systems. Advised IT migration workgroups on cloud computing and GDPR. Managed projects on scope, time and quality with multiple IT stakeholders. Trained my teammates about functional programming, data analysis and system administration. Software stack: - Distributed computing: Spark, PrestoDB, SQL Server - Orchestration systems: Docker - Monitoring systems: Grafana, Datadog - Cloud computing platform: AWS -- Data Engineering, Machine Learning, Cloud Architecture

MFG Labs / Digital consulting Role Overview: Designed and developed automated decision systems for clients across various industries, enhancing their operational efficiency and decision-making processes. Key Projects: - developed rule-based recommender applications to optimize logistics operations. - maintained content management applications integrated with social networks, including legacy software maintenance and rearchitecting data components. - designed ETL processes to support marketing teams with data-driven insights. - built reservation software, recommender systems, and smart speaker integrations to enhance user interactions. Main Clients: - Bolloré Logistics - Warner Bros. France - OUI.sncf Internal Contributions: - advised marketing teams on crafting effective requests for proposals. - authored quality assurance and training documentation for backend developers, focusing on best practices for writing REST APIs using Scala, Node.js, and Spark. Technical stack: - APIs: Facebook (Messenger Platform, Sharing, Login, Analytics), Google (Actions on Google, Dialogflow, Calendar, Login), Algolia, Spotify, Slack, Eulerian, iAdvize, Omniture - Databases: Elasticsearch, PostgreSQL - Distributed computing: Spark, Hive, HDFS - Cloud computing platforms: Amazon Web Services, Google Cloud Platform -- Recommender Systems, Data Engineering, Software Architecture