GERALD CHIDYAUSIKU

• Lead the design and development of BI solutions using MS SQL Server, SSIS, and Power BI to provide actionable insights to business stakeholders.
• Developed complex SQL queries, stored procedures, and triggers to support reporting and data integration processes
• Utilized Azure Data Factory (ADF) to build reusable data frameworks to orchestrate data pipelines and automate ETL processes, ensuring data consistency across multiple systems.
• Designed and optimized complex SQL queries, PL/SQL procedures, and SnowSQL scripts for high-performance data extraction, transformation, and loading (ETL).
• Implemented partitioning, indexing, and performance tuning techniques in Oracle and Snowflake to optimize query performance and reduce execution times by 50%.
• Built data lake ingestion pipelines using Snowflake, integrating structured and semi-structured data (JSON, Parquet, Avro) to support analytics use cases.
• Designed and implemented data models, ERD diagrams, and data flow architectures to support new and existing applications
• Collaborated with product owners and Agile teams to create user stories, define acceptance criteria, and ensure successful sprint delivery
• Mentor junior developers and provide guidance on best practices in SQL development, data integration, and reporting
• Created and optimized complex SQL queries, stored procedures, and views to support reporting and data analysis needs
• Implemented SSIS packages for data extraction, transformation, and loading (ETL) across various data sources
• Streamlined CI/CD Pipelines Developed and maintained Continuous Integration/Continuous Deployment (CI/CD) pipelines in Azure DevOps, automating the deployment of SQL scripts, SSIS packages.
• Version Control and Code Management Managed code repositories using Azure Repos, ensuring version control and collaboration among development teams
• Integrated automated testing into Azure DevOps pipelines, ensuring the quality and stability of SQL scripts and applications before deployment
• Configured release pipelines in Azure DevOps to automate the deployment process across multiple environments, reducing manual intervention and deployment time
• Implemented Infrastructure as Code (IaC) using Azure DevOps and ARM templates, enabling consistent and repeatable deployments of SQL Server instances and related resources.
• Implemented parameterized reports and dynamic filtering in Power BI and Tableau to enhance user interactivity.
• Automated dashboard refresh schedules in Power BI Service and Tableau Server, reducing manual intervention.
• Developed Python and R scripts in Power BI for advanced statistical analysis and forecasting model.
• Developed interactive dashboards and reports in Power BI, Tableau, and QlikView, providing real-time business insights to executives and stakeholders
• Integrated SQL-based data models with BI tools, ensuring accurate data representation and reducing report generation time by 40%.
• Created DAX calculations and custom visualizations in Power BI to improve analytics capabilities for operational decision-making.

• Company Overview: Fannie Mae is a leading provider of mortgage financing in the U.S
• Lead consulting team in implementing a on-premises to Azure Cloud migration
• Created pipelines to migrate the data from on-prem resources through the data lake and load the data into the Azure SQL Datawarehouse
• Migrated data from heterogeneous data sources (Data mart, Access, Excel, Flat file) to centralized SQL server databases using SSIS to overcome transformation constraints
• Played a pivotal role In Migrating the organization's infrastructure to an IaaS solution helping reduce maintenance of on-premises data centers, and gain real-time business insights
• Decreased database load by 26% by refactoring data structures and implementing an update on event cache
• Developed an ETL system to replace an existing data pipeline built on DTS using Python 2.7 and SQL Server
• Leveraged Snowflake's capabilities for data warehousing, analytics, and data sharing within the Azure environment
• Utilized Snowflake's connectors and integration options to seamlessly interact with Azure services such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database
• Developed ETL process to transform the data from different systems using the ADF pipelines and use Data Lake process in order to perform data massaging or conversions
• Utilized Azure Data Factory monitoring tools to monitor ETL pipeline performance and identify bottlenecks resulting in Improved ETL pipeline efficiency by 25% through proactive performance tuning and optimization
• Implemented row-level security and data encryption techniques within Azure Synapse Analytics to ensure data security and compliance with regulatory requirements (e.g., GDPR, HIPAA)
• Defined pipelines in Python, allowing for dynamic pipeline generation writing code that instantiates pipelines dynamically
• Migrated the existing Spark jobs on to Azure Databricks
• Created pipelines to migrate the data from on-prem resources through the data lake and load the data into the Azure SQL Datawarehouse
• Write complex stored procedures to handle the incremental data and doing a merge process to handle SCD type II
• Built scalable and automated ETL pipelines to support enterprise data movement, reducing manual processing by 60%
• Designed data orchestration workflows using Apache Airflow and ADF, ensuring seamless data integration across multiple sources.
• Implemented error handling and logging mechanisms to improve pipeline resilience and reduce downtime.

• Company Overview: Homebridge Financial Services, Inc
• (Homebridge), is one of the top privately held, non-bank mortgage lending firms in the U.S
• Developed Python scripts using PySpark within Azure Synapse Analytics Notebooks to extract data from multiple sources such as Azure Blob Storage, Azure SQL Database, and REST APIs
• Implemented parallel processing techniques in Azure Synapse Analytics Pipelines to transform raw data into a structured format suitable for analysis
• Storing and analyzing massive amounts of structured data from different sources for reporting, analytics, and business intelligence purposes
• Integrated structured and semi-structured data from data lakes for analysis, providing a unified view of data across the organization.
• Designed and optimized complex SQL queries, PL/SQL procedures, and SnowSQL scripts for high-performance data extraction, transformation, and loading (ETL).
• Implemented partitioning, indexing, and performance tuning techniques in Oracle and Snowflake to optimize query performance and reduce execution times by 50%.
• Built data lake ingestion pipelines using Snowflake, integrating structured and semi-structured data (JSON, Parquet, Avro) to support analytics use cases.
• Created automated data loading pipelines in Azure Synapse Analytics Pipelines to load transformed data into Azure Synapse Analytics reducing manual intervention by 80%, leading to faster data availability for business users and improved operational efficiency
• Integrated Snowflake and Azure services, enabling a unified data ecosystem within the Azure environment allowing users to easily move data between the two.
• Migrated ETL processes for mortgage data from on-premises SQL Server to Azure SQL Database using SSIS, reducing data processing time by 30% due to improved scalability and performance in the cloud environment.
• Developed and executed SSIS packages to extract, transform, and load mortgage data from legacy on-premises systems to Azure Data Lake Storage Gen2, achieving a seamless transition to the cloud with zero data loss.
• Implemented incremental data load strategies in SSIS to synchronize mortgage data between on-premises and Azure cloud environments, resulting in a 50% reduction in data transfer costs and improving data consistency and integrity.
• Utilized SSIS for real-time data integration between Azure SQL Database and external mortgage applications, reducing data latency by 70% and enabling timely decision-making for mortgage processing and underwriting.
• Proficient in designing interactive dashboards and reports, data modeling, and DAX queries in Power BI.
• Spearheaded the adoption of GitOps workflows for version-controlled infrastructure and configuration management, ensuring consistency and reliability in mortgage software deployments.
• Established automated testing and quality assurance processes in DevOps pipelines, integrating tools for static code analysis and security testing of mortgage applications.
• Implemented CI/CD pipelines for application delivery, leveraging tools such as GitLab CI/CD, and Azure DevOps to automate build, test, and deploy workflows.
• Designed and implemented data models for data warehousing solutions, including dimensional modeling (e.g., star schema, snowflake schema) and normalization techniques.
• Created ERDs and schema designs to represent data structures and relationships in relational databases e.g MS SQL Server, ensuring optimal performance, scalability, and availability.

• Company Overview: Planet Home Lending is a full-service mortgage expert
• Led a team of developers in optimizing database systems
• Led the establishment of DevOps practices within the mortgage software development lifecycle, fostering collaboration between development, operations, and quality assurance teams to streamline processes and drive efficiency
• Implemented DevOps toolchains and automation frameworks in Azure DevOps, and GitLab, to enable end-to-end automation of mortgage application development, testing, and deployment workflows
• Championed the adoption of infrastructure as code (IaC) principles enabling version-controlled and repeatable provisioning of infrastructure resources for mortgage systems
• Developed and implemented a new data warehousing strategy, increasing data retrieval efficiency by 30%
• Implemented report manager to set up roles, security, and subscripts (data driven/standard) on the existing SSRS reports optimized the design and development of complex reports and dashboards using SSRS and Power BI
• Designed and automated end-to-end CI/CD pipelines using Azure DevOps, ensuring seamless integration and deployment of mortgage software applications across multiple environments
• Orchestrated blue-green deployments and canary releases in CI/CD pipelines to minimize downtime and mitigate risks during mortgage application updates.
• Led root cause analysis efforts for critical data failures, reducing debugging time by 50% through proactive monitoring and alerting.
• Designed and implemented data anomaly detection algorithms to identify and rectify inconsistencies in near real-time
• Created self-healing data pipelines, leveraging automated retry mechanisms and adaptive load balancing for high availability.
• Sound Experience and understanding of SSAS and OLTP cube, Data mining and architecture
• Creating new tables, views, functions, and stored procedures in various complexity, as well as modifying existing structures for front end applications as well as ETL processes and reports
• Enhancing SQL functionality with adding error handling methods such as applying try/catch and if/else blocks and fixing existing errors during execution
• Optimizing existing T-SQL stored procedures, functions, etc
• By modifying code structure or certain statements with low performance, recompiling stored procedure, etc
• With SQL Profiler and information in execution plan
• Collaborated with cross-functional teams to integrate advanced analytics into client solutions, enhancing user experience
• Optimized SQL queries for major clients, Originations and Servicing resulting in a 25% improvement in performance
• Designed and implemented robust database solutions platforms, supporting 10,000+ daily transactions
• Conducted extensive data analysis, providing actionable insights that drove business decisions
• Planet Home Lending is a full-service mortgage expert

Company Overview: Mr Cooper is based in Dallas, Texas and is a proud part of the MrCooper Group (Nasdaq: COOP)Serving 4.6 million homeowners, Mr Cooper is one of the largest home loan servicers in the US.

• Automated ETL processes, making it easier to wrangle data and reducing time by as much as 40%
• Utilized Airflow for workflow orchestration, managing complex data pipelines.
• Designed and developed interactive dashboards in Power BI and QlikView, enabling real-time insights into key business metrics.
• Created custom visualizations and drill-through reports, allowing users to explore data at multiple levels for deeper analysis.
• Utilized advanced DAX and MDX expressions to build complex calculations, improving business performance tracking.
• Developed KPI dashboards that provided executives with at-a-glance insights, reducing manual reporting efforts by 70%
• Developed custom operators and sensors to handle specialized tasks, improving workflow efficiency by 20%
• Successfully monitored and troubleshooted workflows, reducing downtime
• Increased the efficiency of the data fetching by approximately 30% using query optimization and indexing
• Experience in Developing and Extending SSAS Cubes, Dimensions and data source views SSAS Data Mining Models and Deploying and Processing SSAS objects
• Expert in designing complex reports using SQL Server Reporting Services (SSRS) and Excel Pivot table based on OLAP cubes which make use of multiple value selection in parameters pick list, cascading prompts, matrix dynamics reports and other features of reporting service
• Extensive knowledge of designing, developing, and deploying various kinds of reports using SSRs using relational and multidimensional data
• Experienced working with various services in Azure like Data lake to store and analyze the data
• Developed Dashboards and Drilldown reports, Parameterized reports, Linked reports, Ad-Hoc reports, Sub reports and filters, charts in Power BI and SSRS
• Implemented Cloud Security and Data Loss Protection Used Scala to store streaming data to HDFS and to implement Spark for faster processing of data (40% faster).

• Company Overview: Capital One Financial Corporation is an American bank holding company specializing in credit cards, auto loans, banking, and savings accounts, headquartered in McLean, Virginia with operations primarily in the United States
• Utilized innovations to create compelling user stories resulting in accelerated development or mobile and web banking apps
• Implement robust data integration and ETL processes to extract, transform, and load data from diverse sources into a centralized data repository or data lake
• Use data integration tools and platforms to automate data pipelines, streamline data workflows, and ensure consistency and reliability in data processing
• Developed Senior Executive dashboards in Power BI and SSRS providing key metrics Insights, KPIs, Trends, Statistics and correlations for data driven business decision making
• Connected SSRS to data sources such as SQL Server Database, Oracle, Excel files and SharePoint lists
• Used Report designer in Report builder to design reports including tables, charts, graphs and visual elements
• Configure Report Subscriptions to schedule and automate report delivery in SSRS
• Monitor and optimize the performance of SSRS reports and the report server identifying bottlenecks and optimizing query performance
• Conducted validation and UAT testing prior to production utilizing MSP for servicing data and Encompass for originations data validation
• Trained junior analysts on MSP tabs and functionality
• Conducted code review sessions for reporting and analytics team
• Worked closely with external stakeholders, such as customers and system administrators
• Outlined pain points in product requirements resulting in accelerated development of mobile and web banking apps for banks of all sizes, spread across the world
• Clearly defined the objectives of the data process and establish specific requirements related to data quality and Governance
• Provided senior management with KPI's, business insights and trends
• Built macros, performed vlookups and file comparisons in Excel
• Capital One Financial Corporation is an American bank holding company specializing in credit cards, auto loans, banking, and savings accounts, headquartered in McLean, Virginia with operations primarily in the United States