Daphne Liu
Verified Expert in Engineering
Data Modeling Developer
Jacksonville, FL, United States
Toptal member since July 30, 2018
Daphne is a highly motivated big data analytics architect and SQL/Tableau/Power BI, business intelligence developer with strong business analytics solution delivery skills and 20 years of progressively responsible OLTP/OLAP database development/architecture experience. She is a frequent seminar speaker and workshop trainer in business intelligence and analytic solutions. Daphne is experienced in collaborating with business users in data modeling and business analytics solutions.
Portfolio
Experience
- Data Modeling - 15 years
- SQL - 15 years
- Data Warehouse Design - 8 years
- Tableau - 7 years
- Data Virtualization - 5 years
- Big Data - 5 years
- Apache Cassandra - 4 years
- Machine Learning - 3 years
Work Experience
Director, Data & BI
Connsci
- Designed scalable AWS and Snowflake data warehouse/lakehouse architectures and defined enterprise modernization roadmaps. Built AI-ready data platforms supporting Advanced Analytics, self-service analytics & interactive Power BI & Tableau dashboards.
- Developed scalable ETL/ELT pipelines using Python, SQL, Matillion, HVR, APIs, and CDC to integrate healthcare data. Engineered trusted datasets, semantic models, and Power BI/Tableau dashboards supporting operational and executive reporting.
- Built secure, HIPAA-compliant data engineering solutions supporting ML and AI initiatives. Established data quality, governance, CI/CD, and monitoring while collaborating with business and technology teams to deliver scalable enterprise data products.
- Architected scalable AWS and Snowflake data warehouse/lakehouse platforms, modernization roadmaps for enterprise reporting, Power BI/Tableau self-service analytics, Advanced Analytics, and AI/ML, reducing data delivery time and improving data quality.
Principal BI Architect and AI Engineer (Director Level)
Delta Dental
- Led a team of data scientists/engineers in building scalable ML models for churn prediction, lifetime value forecasting, and personalized recommendations.
- Built scalable enterprise data and AI architectures, along with MLOps systems, to reliably run models in production.
- Contributed to ML, AI, and agentic AI to design and deliver practical ML solutions—including time series forecasting, regression, classification, neural networks, and RAG-powered LLMs (ChatGPT-4)—focused on real-world impact and scalable deployment.
- Designed scalable AI/ML system architectures, integrating with data platforms (e.g., Snowflake, Azure ML Studio, Azure AI Foundry, Dataiku, etc.). Defined model lifecycle processes: training, deployment, monitoring, and retraining.
- Standardized feature engineering pipelines, model registries, and reproducibility frameworks. Defined best practices for MLOps, automation, and CI/CD pipeline integration. Led development of complex ML models and generative AI solutions.
- Mentored data scientists and business stakeholders on Tableau best practices and advanced analytics. Integrated machine learning outputs, including prediction scores, anomaly detection, and forecasting insights, into reporting solutions.
- Developed Tableau dashboards to monitor data quality, testing results, and operational KPIs. Leveraged SQL and Python to automate data pipelines, improving efficiency and enabling scalable, self-service analytics across business functions.
Head of Advanced Analytics & AI/ML
CEVA Logistics
- Led enterprise advanced analytics initiatives, translating supply chain challenges into scalable data solutions that improved operational efficiency, cost optimization, and executive decision-making across global logistics networks.
- Built predictive analytics models for forecasting, route optimization, carrier ranking, pricing, and risk prediction, embedding outputs into production workflows and Tableau dashboards to enable real-time, data-driven business decisions.
- Architected Snowflake-based data warehouse solutions, designing scalable multi-layer data models, ETL/ELT pipelines, and governance frameworks to support structured and semi-structured data for enterprise analytics at a global scale.
- Led BI delivery using Tableau, building executive dashboards and operational scorecards for logistics KPIs, including cost, performance, CO2e, and service levels, integrating ML outputs to bridge data science and business insights.
- Managed and mentored data engineers, BI developers, and data scientists, driving standards for cloud data architecture, pipeline scalability, and analytics best practices to ensure high-quality, enterprise-grade delivery.
Principal Advanced Analytics Architect (AI/ML)
CEVA Logistics
- Led a team of data scientists and data engineers in building scalable ML models for route optimization, carrier ranking, and logistics best practice recommendations.
- Contributed to the enterprise AWS Databricks data warehouse, Symantec self-service data model, and ETL/ELT data pipeline design.
- Used Informatica PowerCenter for ETL workflows, transforming MS SQL OLTP to OLAP, refreshing cloud data pipelines, and moving data into Cassandra data lakes. Also worked with AWS S3 and Redshift for scalable analytics and warehousing.
- Implemented both Tableau and Power BI analytic visualization for supply chain management and a freight management system.
- Implemented and completed data modeling of an enterprise data warehouse, data lakes, and ML/AI forecast models.
- Used Facebook Prophet (a time series algorithm), XGBoost, LightGBM, classification, and PyTorch to deliver an ML and AI solution to a logistics ground TMS system.
- Built and deployed a range of AI/ML models—including time series forecasting, regression, classification, neural networks, LLMs, and RAG—to solve real business problems at scale.
- Delivered a data quality solution using PostgreSQL fuzzy string matching and Python FuzzyWuzzy libraries, cleaning data, and creating mapping groups for the machine learning model.
- Designed and architected a supply chain carrier advisor ML solution that includes data labeling, feature selection, hyperparameter optimization, algorithm training, and carrier selection smart choices advisory to the supply chain management team.
- Deployed the supply chain carrier advisor ML model based on TensorFlow TF-Ranking, AutoKeras, and Neural Network algorithms. Over a million records were trained in this model, providing a training result API and batch forecast result references.
BI Architect
City of Jacksonville
- Architected Microsoft Business Intelligence Solutions using SQL Server, SSIS, and SSAS.
- Built SSAS Cube and MDX.
- Developed SSIS and designed a data warehouse.
- Designed and developed a Microsoft Power BI Solution.
BI Architect
Crowley Marinetime
- Built a Microsoft Business Intelligence Solution SSAS Cube for budget and actual.
- Implemented an SSIS ETL from Oracle and DB2.
- Created a TSQL for Crowley Vessel Captain log dimensional data model.
- Developed an SSRS report.
- Implemented SVN source version control.
Project History
Tableau Dashboard Development
https://public.tableau.com/profile/daphne.liu#!/Big Data Cassandra & Solr Document Search
Big Data Cassandra & Elasticsearch Data Warehouse
Dimensional Data Model for Supply Chain Management and Financial Management
Supply Chain Carrier Advisor — Machine Learning Model
I built the AI and ML model from OLAP by labeling data, selecting features and algorithms, POC using AutoML algorithms, and performed the final production deployment using AutoKeras and TensorFlow TF-Ranking. Data was transformed from OLAP to prediction models using Python and Pentaho PDI.
Education
Master's Degree in Computer Information Science & Engineering
University of Florida - Florida
Certifications
Tableau
Tableau
Skills
Libraries/APIs
Pandas, Fabric, PySpark, AutoKeras, TensorFlow Deep Learning Library (TFLearn), PyTorch
Tools
AutoML, Microsoft Power BI, Tableau, Grafana, Pentaho Data Integration (Kettle), Microsoft Excel, Amazon QuickSight, Microsoft Access, H2O AutoML, Apache Solr, ARIMA, Prophet ERP, AWS Glue, Amazon SageMaker, dbt Cloud, Claude Code, Superset, SSAS, Subversion (SVN), Power BI Desktop, Codex
Languages
Python, Transact-SQL (T-SQL), SQL, Snowflake, Python 3, MDX
Frameworks
Data Lakehouse, Apache Spark, Hadoop, AWS HA
Paradigms
OLAP, Database Design, Business Intelligence (BI), ETL, HIPAA Compliance
Platforms
Dataiku, Linux, Amazon EC2, Databricks, Microsoft Fabric, Azure, SolrCloud, Apache Kafka, Pentaho, Hortonworks Data Platform (HDP), Oracle, Amazon Web Services (AWS)
Storage
Microsoft SQL Server, OLTP, Data Pipelines, Data Lakes, MySQL, JSON, NoSQL, Elasticsearch, Amazon S3 (AWS S3), Redshift, Google Cloud, Cassandra, Druid.io, SQL Server Integration Services (SSIS), IBM Db2, SQL Server Reporting Services (SSRS), PostgreSQL
Other
Data Analysis, Apache Cassandra, Big Data Architecture, Data Virtualization, Data Warehouse Design, Data Modeling, Data Architecture, Big Data, Forecasting, Time Series, AWS Database Migration Service (DMS), Database Schema Design, Integration, Data Engineering, Machine Learning Operations (MLOps), Supervised Machine Learning, Data Build Tool (dbt), ELT, Amazon Redshift, Data Taxonomy, Metadata, Data Warehousing, Data Analytics, Data Visualization, DAX, Dashboard Design, Key Performance Indicators (KPIs), Reports, Dashboards, Data Quality, Database Normalization, Informatica, Artificial Intelligence (AI), Classification Algorithms, Data Science, Machine Learning, Neural Networks, Agile Data Science, Linear Regression, Logistic Regression, Reporting, Amazon Bedrock AgentCore, Cloud, Delta Lake, Cursor AI, Data Governance, Data Strategy, Feature Selection, Performance Tuning, Classification, Data, Time Series Analysis, LLM Reasoning, OpenAI GPT-4 API, Azure Data Factory (ADF)
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