Snowflake Expert in Berlin

• 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

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.

• 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.

• 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

• 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.

• 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.

• 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.

• Performed qualitative and quantitative analysis of extracted findings, categorizing themes, evaluating methodologies, and assessing study quality and reliability.
• Produced research reports and evidence summaries communicating key trends, gaps, and opportunities to academic advisors or cross-functional teams.
• Presented findings through well-structured visualizations, tables, and narrative summaries to support decision-making and guide future research directions.

• Built an FRT (Full Resolution Time) data product in dbt and BigQuery with a MECE ticket lifecycle methodology derived from a unified semantic mapping and ordered event stream.

• Delivered reusable macros, modular models, automated unit tests, and a LookML metric layer adopted by Process Improvements and Ops.

• Overhauled FRT experiments using quasi-experimental and pre-post causal analyses to demonstrate that slower resolution affected GMV, enabling shifting from a blanket 70%-in-48h SLA to problem-specific targets and providing the analytical foundation for SLA redesign.

• Architect AI systems across build-vs-buy layers; guide clients on technology selection, evaluation, integration patterns, and governance to reduce risk and time-to-value.
• Implement Azure/Snowflake solutions (RAG pipelines, chatbots, data agents) including ingestion, retrieval, orchestration, and monitoring.
• Partner with stakeholders to translate business needs into deployable AI roadmaps and reference architectures; align with existing data platforms and security controls.

Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.

• Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
• Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
• Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
• Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
• Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.

• Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
• Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
• Identified performance gaps and improved tool adoption by 65%.
• Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
• Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.