Daniel

- Conducted a comprehensive review and analysis of the existing multi-cloud, hybrid IT and data landscape, mapping current state systems, data flows, and infrastructure components to identify bottlenecks, integration challenges, and opportunities for strategic modernization within pharmacy operations. - Led and facilitated a series of strategic workshops engaging diverse stakeholders including IT specialists, business unit leaders, and compliance officers to define clear business requirements, align on data-driven objectives, and collaboratively shape the vision for a future-state data ecosystem. - Architected and documented a robust, scalable target data architecture blueprint aligned with the long-term strategic goals, focusing on enhancing data integrity, ensuring data security, enabling interoperability between analytical and transactional systems, and supporting future adoption of advanced analytics and omni-channel initiatives. - Served as a member of the project steering group, providing expert technical guidance on data strategy, architectural best practices, and risk mitigation, ensuring the phased implementation plan remained congruent with evolving business needs, and stringent regulatory requirements. - Advocated for and guided the integration of modern data technologies and analytical tools within the existing IT environment, navigating legacy system constraints to enhance data accessibility and empower data-driven decision-making for clinical and operational teams. - Ensured all architectural designs and data management strategies rigorously adhered to strategic goals, compliance standards and PII regulations, embedding security-by-design principles to safeguard sensitive information and maintain trust. - Advisor - Data & AI Strategy - Enterprise Data & AI Architecture - Multi Cloud /Hybrid - Data Governance - Compliance - Marketing Automation & CDP - GCP - AWS - Azure - Integrations

- Designed and implemented scalable data architectures tailored to meet strategic business goals within complex, enterprise-scale client ecosystems (Sales & Digital Marketing sector), effectively managing resource, timeline, and technological constraints. - Translated business objectives into actionable data strategies and end-to-end architectures, enabling the launch of key analytics solutions (e.g., customer value segmentation, LTV modeling) and establishing robust data foundations that supported subsequent AI pilot projects / ensured readiness for AI implementation. - Led requirements definition and technical feasibility assessments across multiple engagements by collaborating with diverse stakeholders (from Executive Mgmt to IT), ensuring architectural solutions aligned strategic goals with implementation realities and achieved cross-functional buy-in / facilitated successful project delivery. - Engineered and documented reusable data integration patterns and foundational pipelines, measurably improving data reliability and accessibility essential for marketing automation. - Subcontractor - Enterprise Architect - Sales & Digital Marketing - Data & AI Strategy - Enterprise Data & AI Architecture - Process Development - Stakeholder Management - Data Modeling - SQL Server - BigQuery - Integration - Enterprise Architecture

- Architected and established the technical vision for "Qogai," Odyssey's core analytics platform, defining its scalable hybrid cloud architecture (IBM Cloud, Azure), data strategy, and Lean/Agile operational processes - Served as the primary technical liaison with IBM; advised internal teams and clients on leveraging advanced analytics, including Machine Learning and optimization techniques, to solve complex business problems. - Selected by IBM as a featured speaker at major European and US conferences (2018), presenting on "Full Circle Analytics"? implementing business-focused, best-practice analytics using ML, intelligent agents, and game theory. - Co-Founder - Chief Analytics Officer - Data & AI Strategy - Enterprise Data & AI Architecture - Process Development - Stakeholder Management - Education - Consulting - IBM Cloud - Cognos - Azure - Reinforcement Learning

- Led the definition of enterprise Data & AI strategy, translating requirements gathered from cross-functional stakeholder interviews into a scalable cloud-hybrid architecture blueprint designed for future AI adoption and optimization. - Architected and documented the new cloud-hybrid data platform, redesigning core data pipelines with automated data quality controls that instantly blocked erroneous data, replacing previous month-long manual validation processes and significantly shortening lead times for analytics delivery. - Established a Data Governance framework, defining policies, standards, stewardship roles, and processes covering data quality, lineage/audit capabilities, and security/access controls. - Led the optimization of Data, Data Science & AI project lifecycles using Lean methodologies, resulting in streamlined workflows, measurable quality improvements, and increased organizational efficiency. - Successfully navigated stringent security controls and legacy on-premise protocols to introduce and integrate essential Data Science, Data Engineering, and AI tools (including Plotly, WhereScape, JupyterLabs), enabling team capabilities previously blocked by restrictions. - Implemented data governance controls and processes across key datasets, measurably enhancing data reliability and enabling trusted AI/ML model development while ensuring adherence to security and compliance protocols. - Built GenAI pipelines on GCP Vertex and Snowflake Cortex. - Built LakeHouse architecture on GCP and Snowflake. - Streamlined qualitative anti-fraud analysis by implementing automation with Snowflake Cortex to process large volumes of unstructured public and supervisory data, drastically reducing manual review time and enhancing investigation efficiency. (Anti Fraud) - Re-engineered quantitative analysis workflows for detecting insider trading networks on massive trade datasets, slashing processing times from ~24 hours to minutes through code optimization, efficient tooling, and improved hardware utilization.(Insider Trading Networks)