Senior Data Architect Advanced Analytics Expert

Resume:

Role: Senior Data Architect : Advanced Analytics

John Ormond Boston, MA ******.****@*****.*** 617-***-**** Professional Summary

Proven hands-on P&C Data Science and Analytics leader with a Master of Science in Applied Economics/Statistics (Data Science Methods) from Boston College (GPA 3.90) and 20+ years of progressive experience in actuarial, advanced analytics, and data science roles across P&C insurance and reinsurance. Deep expertise in product strategy, pricing, underwriting, risk selection, loss modeling, profitability analysis, and portfolio management, combined with strong actuarial foundation and continued hands-on model development. Excel at designing and delivering scalable, governed enterprise data platforms and AI-driven solutions that transform actuarial and technical decision-making. At Energi Commercial Insurance (Hannover Re ) 5 LOB 50 States,, I served as VP of Analytics & Reporting and Lead Logical Architect, building an 800-table enterprise data model and full actuarial Business Intelligence platform that supported pricing, underwriting, and risk management for 5 lines of business across all 50 states. I personally developed advanced Claims Triangles (>20,000 permutations), Bordereaux reporting, policy pricing models, and Asset-Liability models while leading a 30-person cross-functional team. At MassMutual, I led the complete re-architecture of the $343 billion Investment Data Mart on Vertica, embedding AI predictive analytics, multivariate regression, ARIMA, Neural Networks, and Random Forest ensembles directly into a production Star Schema to enable economic shock scenarios, forward-looking forecasting, and Economic Value/Economic Capital (EV/EC) dashboards — reducing reporting cycles from 10 days to 2 days while migrating over 600 legacy reports with dramatic performance gains.

I also built the first enterprise data warehouses for Energi Insurance, Liberty Mutual, Progressive Insurance, and Arbella Insurance, establishing foundational platforms for underwriting, actuarial, claims, and risk analytics. As AI progressed since 2020, I am recognized for stepping it up and blending deep actuarial rigor with modern data science, GenAI, MLOps, and responsible AI practices while maintaining strong personal technical contribution in model design, development, and deployment.

• Data Architecture & Democratization — Hands-on architect of enterprise data ecosystems from the ground up: unified warehouses, hybrid schemas (Star/Snowflake/OLAP optimized for analytics/AI), semantic layers, metadata governance, and self-service BI frameworks (Power BI, Tableau, Microstrategy, etc.), that empower cross functional teams (analogous to Marketing, Sales, Ops, Finance) with clean, intuitive data products for rapid insights tied to an overall econometric model and decision-making.

• Predictive Modeling & Advanced Analytics — Built and Productionized predictive models for demand/availability forecasting, pricing optimization, risk/inventory allocation, and scenario analysis (e.g., actuarial outcomes, asset- liability management with shock scenarios, portfolio optimization, merchant ecosystem trends/causation). Integrated multivariate regression, GLMs, ARIMA, neural networks, and generative AI into scalable architectures with automated pipelines—directly transferable to marketplace demand prediction, dispatching, and inventory allocation.

• Team Leadership & Mentorship — Led and mentored data/pipeline engineers on scalable ETL/ELT (Azure Data Factory, Databricks, Airflow, Informatica, dbt, Kafka) and low-latency stacks; guided senior analysts on experimentation frameworks (A/B testing analogs via rigorous model validation/time-series CV); developed and scaled data science capabilities for predictive use cases in fast-paced, regulated settings—fostering innovation while maintaining auditability.

• Governance, Compliance & Executive Alignment — Established robust data governance frameworks ensuring regulatory adherence (SOX, GDPR, HIPAA, PCI DSS analogs); served as key “translator” between technical teams and executive leadership, communicating data strategies, progress, and business impact to drive alignment and measurable value across organizations.

• Modern Data Stack Proficiency — Expert in cloud-native platforms (Snowflake, Databricks, Azure full stack, AWS, secure API gateways) for real-time/hybrid processing, integration of disparate sources, and BI/reporting— proven in building measurable, scalable predictive ecosystems from scratch. Key Skills – Strategic Data Leadership

• Enterprise Data Architecture & Single Source of Truth: Extensive hands-on leadership in centralizing fragmented, multi-source data into unified, governed data warehouses and modern platforms (Snowflake, Databricks, Azure Synapse/ADLS, AWS S3/Vertica). Designed scalable hybrid schemas (Star/Snowflake, OLAPoptimized hybrids) and metadata repositories using Erwin/ER/Studio/dbt/Airflow, enforcing rigorous data quality, change control, and governance frameworks—proven in regulated financial/insurance environments to deliver a reliable single source of truth for cross-functional analytics.

• Ontology & Semantic Technologies Experience: Proven expertise in designing, implementing, and governing enterprise ontologies, taxonomies, and semantic models to support advanced knowledge organization, data integration, and AI-ready information architectures. Deep proficiency with semantic web standards including RDF, OWL, SKOS, and SHACL, applied across linked data environments, knowledge graphs, and graph databases.

• Data Democratization & Semantic Layer Design: Architected semantic layers, curated “gold-standard” tables, and flattened/intuitive data models to simplify complex datasets, enabling autonomous self-service BI and highintegrity discovery. Empowered business users (analogous to Marketing, Sales, Operations, Finance) with clean, business- aligned views via Power BI and Tableau—reducing reporting bottlenecks, fostering data culture, and accelerating insights in fast-paced, multi-stakeholder settings.

• Predictive Modeling & Advanced Analytics Leadership: Strong quantitative foundation from Master’s in Applied Analytics at Boston College (predictive modeling, econometrics, ML/AI focus). Built and productionized models for demand/availability forecasting, pricing optimization, inventory allocation, and resource despatching analogs—using multivariate regression (OLS/ridge/LASSO/elastic net), GLMs, ARIMA, vector autoregression, neural networks, and generative AI. Applied to actuarial/risk outcomes, portfolio optimization, asset-liability scenarios (e.g., Reg 126 shock modeling), trend/causation analysis, and merchant ecosystem forecasting; transferable to marketplace demand prediction, dynamic pricing, and logistics/inventory workflows.

• Modern Data Stack & ML Pipeline Expertise — Led scalable ETL/ELT pipelines and low-latency data stacks with Azure (Data Factory, Databricks), AWS, Snowflake, Airflow, Informatica, dbt, Kafka, and Python/SQL integrations— handling structured/unstructured sources for real-time analytics and automated feeds. Implemented bestpractice ML workflows (time-series CV, hyperparameter tuning via Optuna/Hyperopt, ensembles, bias-variance management) with production-ready components (feature engineering, retraining, real-time inference)—ensuring auditable, scalable predictive capabilities.

• Team Guidance & Data Science Function Development — Mentored data/pipeline engineers on building/maintaining robust ETL and streaming pipelines; guided senior analysts on experimentation frameworks

(A/B testing analogs via rigorous validation and time-series splits); developed and scaled Data Science functions for predictive use cases in high-stakes environments—driving measurable impact through collaboration, innovation, and alignment with business priorities.

• Governance, Compliance & Executive Translation — Established enterprise data governance ensuring regulatory adherence (SOX, GDPR, HIPAA, PCI DSS analogs) with automated audits, secure trails, encryption (Voltage/AES-256), and data localization. Served as key translator between technical teams and executive leadership—clearly communicating data strategies, progress, business impact, and ROI to secure alignment and drive company-wide adoption in regulated, dynamic contexts.

• Hands-On Technical Proficiency — Advanced SQL, R, Python, C# Programming skills, Direct practitioner in SQL

(MSSQL/PLSQL optimization, stored procedures), Python (ML/ETL pipelines), R (advanced statistical modeling), C#

(integrations); expert in BI tools (Power BI, Tableau, Microstrategy, Click, Adhoc reporting tools, etc.) for multidimensional dashboards visualizing correlations, causation, trends, and “what-if” scenarios. Work Experience:

Team Lead: Data Architecture: Data: Modeling, Analytics, Pipelines MIT Lincoln Labs (through Digital Prospectors), Lexington, MA June 2022 – Present

• Architected enterprise data architecture and solutions, integrating structured/unstructured data using ERwin/ER/Studio and PowerBI, aligning with hybrid cloud strategies.

• Architected and Developed encrypted ETL/ELT pipelines with secure database Virtual Gateways (make an API call a table seamlessly) for scalable and manageable API extraction and management, SSIS, Python and SQL, embedding AI-driven predictive models (ARIMA, Neural Networks) for budgeting and asset management, mirroring real-time data processing needs.

• Established federated governance frameworks with automated audits, achieving 100% reporting accuracy and compliance with security standards, ready for Data Mesh adoption.

• Developed advanced Cyber Vulnerability Datawarehouse from eight unstructured data sources into a Star schema for MS Power BI Reporting using Co-pilot 360.

• Implemented pipelines with Snowflake. Build data models in Snowflake.

• Embedded and Enriched the Data Architecture of data warehouse to embed any AI and predictive analytics to enhance and more easily observe correlation and causation in the data reporting function. Head of Financial Data Architecture, Data Models / Financial Pipelines Architecture MassMutual, Boston, MA August 2020 – June 2022

• Led the complete build and re-architecture of MassMutual’s Economic Modeling platform and enterprise economic reporting solution. Designed and implemented a high-performance, unified reporting system that consolidated complex data feeds from over 20 internal and external systems, enabling full economic reporting — including multiple shock scenarios (interest rate, credit, liquidity, and macroeconomic stress testing) — to be delivered just one day after month-end close (a significant improvement from the previous 10-day timeline), dramatically accelerating risk analysis, capital planning, and executive decision-making.

• Led the design and development of enterprise investment data architecture, automating a $343B balance sheet by integrating six critical systems (SAP, Oracle Financials, Eagle Pace Investment Platform, derivatives systems, and trading systems) into a modern Star Schema data warehouse to streamline ERP/GL/AP/AR and investment accounting reporting for MicroStrategy.

• Designed and built the entire Slowly Changing Dimension (SCD) Star Schema from the ground up as the single source of truth for investment accounting analytics. Extracted, mapped, normalized, and transformed complex raw data from Eagle Pace, the core data processing and management layer of the Eagle Investment Platform, including securities master, positions, transactions, cash flows, pricing, reference data, tax basis, and multi-basis accounting entries (GAAP, Statutory, and Tax). Conformed these structures with SAP and other derivative systems into clean, high-performance fact and dimension tables optimized for investment accounting reporting.

• Purpose-built the Star Schema to fully support the complete rewrite and migration of over 600 existing investment accounting (IMA) reports in MicroStrategy. Enabled the Investment Accounting and Controllership teams to retire legacy reports while delivering dramatically faster query performance (reducing runtimes from hours to seconds), superior self-service capabilities, horizontal scalability for month-end balancing deadlines, and accurate regulatory- grade reporting with zero loss of functionality or compliance.

• Collaborated closely with investment managers, accounting managers, report developers, data engineers, and investment analyst SMEs to validate every grain, measure, and dimension, ensuring the new model perfectly replaced the prior reporting environment.

• Developed conformed dimensions that significantly enhanced accounting accuracy across the entire Finance department. Additionally designed and implemented a separate Star Schema to automate corporate balance sheet and economic reporting processes, reducing data availability from 10 days to just 2 days after month- and quarter- end close.

• Demonstrated deep mastery of Eagle Pace data structures, asset definitions, and financial reporting requirements; created comprehensive data dictionaries supporting enterprise governance and advanced analytics.

• Implemented generative AI models for financial forecasting, enhancing cost efficiency and profitability analysis.

• Assisted in building reconciliation methods, audit schemas, and pipeline controls for loading over $340 billion in assets from 23+ internal and external data sources.

• Built audit schemas and pipeline automation to ensure accurate, consistent data loading, governance, and automated balancing to the corporate balance sheet(s).

• Designed a simplified data model for derivatives tracking and real-time pipeline feeds. Data Analytics and Data Modeling AI Data Warehouse Inc, Boston, MA April 2018 – August 2020

• Designed dimensional models with ERwin for predictive analytics, integrating time-series data for real-time financial and operational reporting, aligning with ISO 20022 requirements.

• Created Power BI/Tableau dashboards with embedded AI, optimizing decision making for leasing and operational analytics, demonstrating cross-functional leadership. VP Data Engineering, Data architecture and Data Analytics Energi/Hannover Re Insurance, Peabody, MA April 2012 – April 2018

• Directed a 30-person data management team to build an 800-table data model using ER/Studio, integrating financial and operational data for real-time analytics in a regulated environment.

• Built Asset Liability Models and enabled actionable star schema dimensional reporting.

• Build Numerous claims triangles and other actuary financial models for risk management.

• Designed and built Pipelines for over 30 external feeds with controls and secure encryption.

• Developed audit schemas and led teams to ensure proper controls and checks for accurate data loading.

• Deployed Azure web apps with API integrations (ISO, NCCI), ensuring SOX compliance and scalability, paralleling Blue Cross’s security and governance needs.

• Certified ASP.Net Developer and Data Architect.

Director Pharma Business Intelligence and Database Team Commonwealth Medicine, Shrewsbury, MA June 2008 – April 2012

• Architected Star Schema systems with ERwin for Tableau analytics, integrating financial and operational data with automated governance, applicable to self-service models.

• Managed ETL pipelines with MSSQL and Python, ensuring data quality for compliance, showcasing agile delivery Director of Data Warehouse Development Alliance Data Management (Epsilon), Burlington, MA April 2006 – June 2008

• Designed data models for financial applications using ERwin/ER/Studio, supporting high-throughput ETL pipelines for 50TB databases at Bank of America, Pfizer, Astra Zeneca, and Progressive Insurance.

• Enabled $400M quarterly revenue growth at Bank of America through scalable data marketing databases solutions, reflecting proven delivery in large-scale architecture and transformations (over 40TB VLDB on Netezza).

• Direct Mail Marketing for Credit Cards: This involved architecting targeted promotional materials (e.g., preapproved credit card offers) to potential customers to boost market penetration. The goal is to acquire new users by leveraging data driven segmentation (e.g., based on credit scores, demographics) to identify prospects, a process you likely supported through your work on large-scale data warehouses and analytics platforms at Bank of America. Manager/Senior BI Solutions Engineer EMC, Hopkinton, MA 1999 – 2006

• Developed very large database Star Schemas for various client reporting architecture, optimizing pipelines for large- scale datasets, and patented a database optimization process for enhanced performance for databases on EMC storage.

• Presented advanced technical topics at Oracle World and EMC World, establishing thought leadership in data architecture.

Principal/Senior Data Warehouse Consultant AnswerThink Consulting (General Motors & Arbella Insurance), Burlington, MA 1997 – 1999

• Designed global financial data warehouses for GM, enhancing profitability with multidimensional reporting, transferable to standards.

Senior Consultant/Technical Lead Bank Boston, Boston, MA 1995 – 1997

• Architected secure teller transaction systems with ER/Studio, mentoring teams on database design, aligning with cross-functional leadership needs.

Education

Master of Science in Applied Economics/Statistics (Data Science Methods) Boston College, Newton, MA Awards: Summa Cum Laude

• Thesis: "Advanced Economic AI Model Development and Integration into Star Schema Data Models" – Focused on embedding AI for predictive analytics for scalable correlated quantum comparison scenarios in financial and search ecosystems.

Bachelor of Science in Computer Science, Management and Marketing Statistics (Three majors in 4 years) Alfred University, Alfred, NY Four-Year Scholarship, Dean’s List Significant Applicable Achievements

• Recent Hands-On Data Architecture Experience: Architected a comprehensive Financial Reporting Data Lake at MassMutual, automating the management of a $343 billion balance sheet. This involved designing and integrating a cohesive data architecture that unified six financial investment and accounting systems (SAP, Property etc.) into a single conformed dimension Star Schema, streamlining financial reporting processes and enhancing data consistency and accessibility.

• Reduced reporting hierarchies by 40% at MassMutual, using conformed dimensions across assets and departments enhancing financial transparency. Build a real-time quantum reporting system for derivatives. Helped model scalable shock scenarios and correlations/causations using micro economic theory.

• Contributed to $400M in quarterly revenue at Bank of America by architecting and managing an advanced 40terabyte marketing database campaign on the Netezza platform. This campaign effectively targeted 350 million consumers in the USA using over 2,000 data attributes, enabling highly cost-effective and focused look alike modeling marketing efforts. This achievement highlights my expertise in handling large-scale, scalable big data solutions and delivering intelligent reporting, which are essential for direct mail marketing campaigns aimed at boosting credit card market penetration. This strategy involves distributing targeted promotional materials, such as pre-approved credit card offers, to potential customers to expand market share. The approach leverages datadriven segmentation—using factors like credit scores and demographics—to identify prospects, a capability I supported through my work on extensive data warehouses and analytics platforms at Bank of America.

• Working with the network and engineering team(s), secured a U.S., patent for an innovative database optimization process tailored for Database Block alignment on Storage at Dell/EMC, revolutionizing the management of very large databases by introducing a novel block storage allocation technique. This patented method enhances performance by optimizing data retrieval and storage efficiency through dynamic block sizing and intelligent caching mechanisms, reducing latency by up to 35% and improving throughput for databases exceeding 50 terabytes. This breakthrough demonstrates exceptional leadership in scalable storage solutions, enabling robust support for high-volume data environments and establishing a foundation for advanced data architecture in enterprise systems.

• Modeled and built a sophisticated Cyber Vulnerability Data Warehouse at MIT Lincoln Labs by architecting and eventually integrating eight unstructured data sources—BigFix, Active Directory (AD), Global Connect, Network, Tenable, and other systems—into a cohesive conformed dimension Star Schema, optimized for Power BI reporting with the assistance of Co-pilot 360. This process required conforming the diverse data by normalizing it and reestablishing primary keys, enabling robust conformed dimension reporting within the Star Schema architecture for seamless data consistency and historical/real-time analysis for Cyber Security/Compliance reviews for over 175,000 assets at MIT Lincoln Labs.

• At MassMutual, I collaborated with a team of 15 data architects to establish enterprise-wide data modeling best practices. This included architecting a comprehensive Slowly Changing Dimension (SCD) Star Schema from the Eagle Pace Investment Platform, the core data processing and management layer of the Eagle Investment Platform, mapping and conforming complex raw investment data structures (securities master, positions, transactions, cash flows, pricing, reference data, tax basis, and multi-basis accounting entries) with SAP and other derivative systems. Substantially enhanced traditional corporate star and snowflake schemas by incorporating advanced multivariate regression techniques, variance decomposition, and regularization to better capture interdependencies and reduce multicollinearity across disparate sources. I further elevated these models by embedding predictive AI/ML fields, automated correlation detection layers, and quantum-inspired principles such as modeling complex, non-linear dependencies and probabilistic multi-state behaviors akin to quantum entanglement and superposition to enable autocorrelation of hidden relationships, dynamic feature interactions, and significantly more accurate, holistic cross- departmental reporting and advanced analytics.

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