portfolio
Jason Koch
DATA & ANALYTICS LEADER
Enterprise data architecture, platform modernization, and AI-driven analytics across financial services.
Executive Summary
Jason Koch is a senior Data & Technology leader with over 30 years of experience architecting and delivering enterprise data platforms across asset management, banking, lending, and risk environments.
He currently serves as Director of Data Engineering at Champions Funding, where he is standing up Azure data platforms, operational monitoring, and least-privilege security for a new line of business. Previously at DoubleLine Funds, he led large-scale modernization—including Microsoft Fabric and Data Vault architectures supporting multi-terabyte environments and over $150B in assets under management.
Jason combines deep technical expertise with executive leadership, aligning data strategy to business outcomes while driving scalable, governed, and high-performance data platforms. He has extensive experience leading globally distributed engineering teams and delivering solutions within highly regulated financial environments.
He is recognized for building resilient, enterprise-grade data foundations that accelerate insight delivery, strengthen governance and auditability, and enable long-term platform scalability.
CASE STUDY
DoubleLine Funds
Enterprise Data Platform Modernization
OVERVIEW
Modernizing a $150B Asset Management Data Platform
Led the modernization of a mission-critical data platform supporting investment, risk, and operations across a $150B asset manager. Delivered a scalable, governed lakehouse architecture that improved data reliability, performance, and analytics delivery.
CONTEXT
Enterprise data ecosystem at scale
The platform operated across a complex ecosystem of trading, pricing, accounting, and risk systems, integrating diverse internal and external data sources. Legacy pipelines and inconsistent data quality created growing challenges in scalability, governance, and timely analytics.
PROBLEM
Limited Access to Scalable Analytics
Business users lacked self-service reporting capabilities, relying heavily on engineering teams for data access and insights. Data latency, limited semantic modeling, and tightly coupled pipelines constrained the organization’s ability to scale analytics and respond quickly to evolving business and regulatory demands.
APPROACH
Cloud-Based Data Platform Modernization
Led a platform modernization initiative to transition core data assets to a cloud-based architecture, enabling the adoption of Microsoft Fabric and a data fabric approach. Designed and implemented a scalable semantic layer to support Power BI self-service reporting, while restructuring data pipelines to improve reliability, performance, and maintainability. Established patterns for governed data access and reusable data models across domains.
OUTCOME
Enterprise Self-Service Analytics Enabled
Enabled enterprise-wide self-service analytics through a unified semantic model, significantly reducing dependency on engineering teams. Improved data availability and performance, supporting timely and accurate reporting across investment, accounting, and risk functions. Established a scalable, cloud-based foundation for continued data growth, governance, and advanced analytics capabilities.
Modern Data Platform Architecture
End-to-end data flow from ingestion to consumption across a modern enterprise platform
INGESTION
Data Sources
- APIs
- Flat Files
- Vendors
Trades · Prices · Market Data
Pipelines
- Batch Processing
- Streaming Ingestion
- Change Data Capture (CDC)
Validation
- Quality Rules
- Schema Validation
- Pipeline Monitoring
CORE PLATFORM
Storage
- Lakehouse (Raw / Bronze)
- Warehouse (Curated / Silver)
- Data Marts (Gold)
Trades · Positions · Market Data
Processing
- Transformations (ADF, dbt)
- Orchestration (Pipelines)
- Data Modeling (Dimensional, DV)
Risk · P&L · Aggregations
Serving
- Semantic Models
- APIs / Data Services
- Feature Store
CONSUMPTION
Analytics
- Dashboards (Power BI)
- Ad-hoc Analysis
- Reporting
Risk · Portfolio Analytics
Data Products
- APIs / Data Services
- Embedded Analytics
- Data Sharing
Data Science
- Machine Learning Models
- Feature Engineering
- Experimentation
CORE CAPABILITIES
Enterprise Data & Analytics Leadership
Strategy, architecture, and delivery of enterprise-scale data platforms across financial services and regulated environments.
DATA STRATEGY & PLATFORM MODERNIZATION
Define enterprise data strategy and modernization
Transform legacy warehouse environments into scalable, cloud-native platforms, including Microsoft Fabric lakehouse architectures and distributed data ecosystems.
ENTERPRISE DATA ARCHITECTURE
Design scalable, resilient enterprise data architectures
Architect Data Vault, dimensional, and medallion-based platforms that establish standards for integration, modeling, and lifecycle management across complex data ecosystems.
DATA ENGINEERING & DELIVERY
Lead high-performing data engineering organizations
Direct distributed teams delivering production-grade pipelines and platforms, implementing orchestration, CI/CD, and monitoring frameworks to ensure reliability and scalability.
DATA GOVERNANCE & QUALITY
Establish governance, quality, and regulatory alignment
Implement data governance frameworks, lineage, and validation controls that ensure accuracy, auditability, and compliance across financial and risk data environments.
ANALYTICS & BUSINESS ENABLEMENT
Enable analytics and business-driven decision making
Deliver semantic models, executive reporting, and analytics platforms that translate complex data into actionable insight across investment, risk, and operations functions.
Executive Positioning
Bridging executive strategy with hands-on architecture and engineering execution across enterprise platforms.
- 30+ years delivering enterprise data platforms in financial services and regulated environments
- Led multi-region engineering teams across U.S. and offshore delivery models
- Deep expertise in Microsoft Fabric, Data Vault, and modern data architectures
- Improved data reliability, performance, and reporting latency at scale
- Balances strategic leadership with hands-on execution
LEADERSHIP & IMPACT
Building Teams That Deliver With Clarity, Trust, and Accountability
Leadership grounded in clear direction, strong engineering standards, collaborative decision-making, and developing people who can own outcomes.
LEADERSHIP
Building High-Performance Data Organizations
I build and lead data organizations that operate as strategic partners to the business, aligning platform capabilities with enterprise objectives. My focus is on creating scalable operating models, establishing clear ownership, and enabling teams to deliver consistently in complex, regulated environments.
OPERATING MODEL
Designing for Scale, Accountability, and Delivery
I design operating models that balance centralized governance with distributed execution. This includes defining clear roles across architecture, engineering, and analytics, implementing delivery frameworks, and ensuring alignment between business stakeholders and technical teams.
EXECUTIVE ENGAGEMENT
Translating Strategy into Execution
I partner closely with executive leadership to translate strategic objectives into actionable data initiatives. This includes aligning investments to business priorities, communicating complex architectures in clear terms, and ensuring measurable outcomes from data programs.
IMPACT
Enterprise Leadership Impact
- Built and scaled data engineering organizations supporting enterprise analytics platforms
- Led multi-region teams across U.S. and offshore delivery models
- Delivered large-scale data platform modernization initiatives in regulated financial environments
- Improved data reliability, performance, and reporting latency across critical systems
- Established governance, quality, and control frameworks supporting enterprise data operations
- Enabled analytics and reporting platforms supporting investment and risk management functions
PLATFORM EXPERTISE
Enterprise Data Platform Capabilities Across Architecture, Engineering, and Analytics
Deep expertise across modern data platforms, engineering frameworks, and analytical systems, with a focus on building scalable, reliable, and business-aligned data capabilities.
DATA PLATFORMS
Cloud-Native Data Platforms
Microsoft Fabric, Azure Data Services, SQL Server, Lakehouse and Warehouse architectures, medallion design patterns, distributed storage and compute frameworks.
DATA ENGINEERING
Scalable Data Pipelines and Processing
ETL/ELT pipeline design, orchestration frameworks, real-time and batch processing, data integration patterns, performance optimization, and operational reliability.
DATA ARCHITECTURE
Enterprise Modeling and Design
Data Vault 2.0, dimensional modeling, canonical data models, metadata-driven architecture, domain-oriented design, and scalable enterprise data frameworks.
ANALYTICS
Analytics and Semantic Layers
Power BI, semantic modeling, reporting frameworks, executive dashboards, self-service analytics, and data delivery aligned to business functions.
CAPABILITY
End-to-End Platform Expertise
- Experience spanning platform architecture, engineering, and analytics delivery
- Bridging legacy systems and modern cloud-native data platforms
- Designing for scalability, performance, and governance in regulated environments
- Aligning platform capabilities to business outcomes and decision-making
DATA ARCHITECTURE & METHODOLOGIES
Architecture Built for Change, Scale, and Trust
Enterprise architecture should create durable foundations while allowing platforms, analytics, and business requirements to evolve independently.
DATA VAULT 2.0
Scalable, Auditable Data Modeling
Designed and implemented Data Vault architectures to support scalable, auditable, and extensible data platforms. Enables historical tracking, flexibility in evolving data models, and strong alignment with enterprise data governance requirements.
MEDALLION ARCHITECTURE
Layered Data Refinement and Processing
Implemented bronze, silver, and gold data layering strategies to standardize data ingestion, transformation, and consumption. Supports data quality, traceability, and performance optimization across analytical workloads.
METADATA-DRIVEN DESIGN
Automation and Standardization at Scale
Developed metadata-driven frameworks to automate data pipeline generation, enforce standards, and improve consistency across platforms. Enables rapid scalability and reduces manual development overhead.
DATA GOVERNANCE
Control, Quality, and Compliance
Established governance frameworks including data quality controls, lineage tracking, and regulatory compliance mechanisms. Ensures trust, reliability, and accountability across enterprise data ecosystems.
APPROACH
Architecture as a Strategic Foundation
- Applying structured methodologies to ensure scalability, flexibility, and long-term sustainability
- Balancing architectural rigor with delivery speed in enterprise environments
- Designing systems that support both operational efficiency and analytical insight
- Aligning platform capabilities to business outcomes and decision-making
SELECTED ACHIEVEMENTS
Delivering Measurable Impact Across Enterprise Data Platforms
Proven results in building, scaling, and optimizing data platforms, with measurable improvements in performance, reliability, and business outcomes.
AZURE PLATFORM BUILD
Stood Up a Full Azure Data Environment in Under a Month
At Champions Funding, delivered SQL Database, ADF pipelines, linked services, integration runtimes, Log Analytics, Key Vault, and supporting platform components for a new line of business.
Enabled a governed, least-privilege Azure footprint by aligning Entra ID groups with ADF and SQL Server roles from day one.
OPERATIONAL MONITORING
Built ADF and Platform Observability with Automated Response
Implemented monitoring for Azure Data Factory, Integration Runtime metrics, and database health, with Email and Teams alerting plus severity-based Jira ticket creation.
Created Azure workbooks and dashboards and used KQL against Log Analytics to investigate pipeline and platform events faster.
DOCUMENTATION PLATFORM
Delivered an AI-Administered MkDocs Documentation System
Built a MkDocs Material documentation library as a system-versioned repository of runbooks, discovery and decision workbooks, project documentation, and a full change log—integrated with Jira.
Implemented GitHub Actions CI with environment URL promotion across development, test, and production, plus 100+ Python build tests on preview promotion.
PLATFORM MODERNIZATION
Led Enterprise Data Platform Modernization
Directed the redesign of legacy data architecture into a scalable, cloud-aligned platform supporting enterprise analytics and reporting.
Reduced data processing latency by over 60% and improved platform reliability across critical reporting systems.
TEAM SCALING
Built and Scaled Data Engineering Teams
Established and led multi-region engineering teams across U.S. and offshore delivery models.
Scaled delivery capacity across distributed teams, improving throughput and reducing development cycle times.
ANALYTICS ENABLEMENT
Enabled Enterprise Analytics and Reporting
Delivered semantic models and reporting platforms supporting investment and risk management functions.
Accelerated access to data and improved decision-making, reducing report delivery time from days to minutes.
DATA RELIABILITY
Improved Data Quality and Reliability
Implemented governance frameworks, validation processes, and monitoring systems across enterprise pipelines.
Reduced data defects and significantly increased trust in enterprise reporting and analytics systems.
PERFORMANCE OPTIMIZATION
Optimized Data Processing and Performance
Refactored ETL pipelines and optimized data processing frameworks across large-scale systems.
Reduced pipeline runtimes and improved operational efficiency across critical data workloads.
ARCHITECTURE IMPLEMENTATION
Implemented Scalable Data Architecture Frameworks
Designed and deployed Data Vault and layered architecture models supporting enterprise data growth.
Enabled long-term scalability for multi-terabyte datasets and evolving business requirements.
TESTIMONIALS
Leadership That Builds Trust, Capability, and Results
Feedback from engineers, partners, and colleagues across enterprise data and analytics organizations.
“Jason is one of the strongest technical leaders I have worked with. He combines deep hands-on engineering expertise with exceptional leadership and a clear vision for building reliable, well-designed systems.”
Manish Gupta
Software Engineer · DoubleLine Funds
“Jason leads with empathy, clarity, and trust. He created an environment where ideas flowed freely, challenges were approached with confidence, and every team member felt supported.”
Jyoti Yadav
Data Engineer · DoubleLine Funds
“Working for Jason at DoubleLine was profoundly educational and rewarding. He is a rare leader who is constantly learning himself while actively fostering growth in his team.”
Akshay Jajoo
Data Engineer · DoubleLine Funds
“Jason is an exceptional leader and mentor who leads by example. His calm leadership, thoughtful planning, and deep trust in his team helped us consistently deliver on challenging work.”
Seema Shekam
Data Engineer · DoubleLine Funds
CREDENTIALS
Education and Professional Certifications
Formal education and continuous professional development in data engineering, architecture, and AI.
EDUCATION
Arizona State University
Master of Business Administration (MBA)
Arizona State University
Master of Science, Information Management
Arizona State University
Bachelor of Science, Computer Information Systems
Summa Cum Laude
CERTIFICATIONS
Microsoft Certified Fabric Data Engineer Associate
Microsoft
Certified Data Vault Data Modeler
Genesee Academy
MCSA: SQL Server Development
Microsoft
CONTACT
Let’s Build What’s Next
Available for executive data leadership, enterprise architecture, and platform transformation initiatives.
Focused on building scalable, enterprise data platforms that drive measurable business outcomes.