Snowflake Experts in Berlin

• Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
• Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
• Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
• Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.

Specialize in turning complex data from diverse environments into actionable business value through compelling visual storytelling. I am an expert in generating actionable insights and presenting recommendations to business stakeholders. My technical proficiency in SQL, Python, and leading data visualization tools like Tableau and Power BI allows me to deliver a new generation of self-service tools and analytics services.

• Data Visualization & Storytelling: Created impactful data visualizations and dashboards in Tableau and Power BI, effectively communicating findings and presenting actionable recommendations to C-suite stakeholders and business leaders.
• Stakeholder Management: Built effective working relationships with key business stakeholders, data engineers, and other partners to achieve common data-driven goals and targets.
• Insights & Recommendations: Generated actionable insights from complex data analysis for funnel conversion, marketing performance, and ROI, directly influencing business performance and strategy.
• Data Collaboration & Empowerment: Worked closely with cross-functional teams to support the ongoing data needs of internal partners, helping to optimize internal data processes and workflows.
• BI & Data Expertise: Applied extensive experience in data modeling, data collection, data mining, and analysis to deliver end-to-end analytical solutions from stakeholder discovery to production.

• Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
• Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
• Designed and implemented evals and observability through Mastra studio.
• Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
• Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.

• Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
• Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
• Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
• Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
• Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
• Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
• Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.

Metrify - OS & Analytics Layer Team:

• Built data architecture foundations through pre-commit contracts and modern dbt metadata structure
• Built and maintained modular layers for smart-meter rollout ops, codifying business rules into Star dwh.

Enpal - Central Data and Platform Team (SCM & Finance):

• Designed and built dbt models delivering a curated analytics layer for SCM and Finance stakeholders.
• Implemented inventory reconciliation across sources (ERP vs 3PL; snapshots vs movements) and surfaced variance drivers at SKU/location/channel level.
• Developed Weeks-of-Stock KPI and stock depletion projection (demand + inbound supply) to support planning and operational decisions.

• Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
• Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
• Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
• Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
• Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
• Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
• Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
• Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
• FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
• Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.

• Directed the contracts management function for Europe's largest renewable energy portfolio, 10 FTE
• Implemented process automations by architecting scalable, data-driven workflows leveraging AI and traditional automation

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

• Taught Python, Pandas, data analysis, visualization and Power BI to 250+ students.
• Guided practical projects from data preparation and exploratory analysis through visualization and dashboard creation.
• Explained complex analytical concepts clearly to audiences with different levels of technical experience.

• Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
• Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
• Exploring Agentic AI & LLM-based tooling for production readiness patterns

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
• Work with customers to understand their challenges and provide the best solutions based on open-source data products
• Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
• Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
• Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
• Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
• Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
• Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible

• Led the end-to-end delivery of a SAP Supply Chain Management (SCM / TD / SD) ERP programme, covering project initiation, detailed requirements gathering, operating model definition, system design, build, testing, cutover, and global Go Live across Europe, Asia, and North America. Ensured the ERP solution supported key supply chain, manufacturing, and planning operations to enable future business growth.

• Conducted cross-functional workshops with Supply Chain, Procurement, Planning, Manufacturing, and Logistics teams to capture business requirements, define the future operating model, and map end-to-end system design. Consolidated over 150 requirements into structured documentation aligned with SAP standards.

• Shaped solution design and vendor engagement during the early discovery phase, supporting selection of best-fit technology partners and ensuring the system design covered production planning, inventory management, warehousing, logistics, and supply chain forecasting.

• Managed D365 configuration and troubleshooting, ensuring alignment with business processes and resolving integration issues between D365, SAP SCM modules, and surrounding systems.

• Supported Grain data model changes to lead ingestion of planning data into Snowflake and Footprint, enabling enterprise reporting and analytics development.

• Managed scope, timelines, risks, and dependencies across international teams spanning Europe, Asia, and the US, maintaining integrated project plans, issue logs, and executive reporting to drive stakeholder alignment and delivery momentum.

• Enabled the integration of AI-powered demand forecasting tools into supply chain planning processes, improving forecast accuracy, inventory turnover, and operational decision-making across multiple regions.

• Led SIT, UAT, and data migration phases, including design of test scenarios, defect triage management, and coordination of test execution to validate supply chain and manufacturing workflows prior to deployment.

• Delivered detailed cutover planning, business readiness activities, and hypercare support, ensuring a smooth and coordinated Go Live and full operational handover to business teams.