Hi, I'm Jonathan Wandera — A Data Architect, Designing Scalable Data Platforms, Warehouses & Analytics Systems.

I specialize in designing and building modern data platforms that power analytics, machine learning, and business intelligence at scale. My work focuses on building reliable data pipelines, scalable data warehouses, and well-modeled datasets that enable organizations to make confident, data-driven decisions.

With experience across the modern data stack, I design architectures that support large-scale ingestion, transformation, and analytics workloads. My work includes building batch and streaming pipelines, implementing dimensional and enterprise data models, and enabling self-service analytics across organizations.

I am particularly passionate about data platform architecture, data modeling strategy, and building systems that make high-quality data accessible to engineers, analysts, and machine learning teams. My focus is always on delivering measurable business impact through scalable and maintainable data systems.

“Transforming complex data ecosystems into reliable, scalable platforms that unlock real business value” is my motto

22+

Years of
Experience

250+

Data Models
Designed

50+

Data Pipelines
Built

999+

TB of Data
Processed

My Career

Experience


Staff Data Engineer / Information Management Specialist

U.S. Department of State — Uganda Mission

(2021 – Present)

Role

Lead data platform engineering initiatives supporting operational analytics and program monitoring for the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). Design scalable enterprise data platforms that integrate operational systems and enable data-driven decision making across mission leadership, program teams, and international partners.

Key Accomplishments

  • Architected and deployed a self-service enterprise data lake integrating 10+ operational systems, enabling cross-organizational analytics through standardized ingestion pipelines and scalable data models.
  • Reduced reporting latency by 60% across leadership dashboards by building automated ELT pipelines with modular transformations and orchestrated workflows.
  • Engineered high-reliability data pipeline infrastructure with sandbox testing environments, automated data validation, and observability monitoring.
  • Enabled analytics for $2B+ public health investments by consolidating operational, donor program, and logistics datasets into centralized analytics models.
  • Led cross-team architecture initiatives across U.S. and international stakeholders to align data infrastructure with strategic planning and program monitoring requirements.

Enterprise Data Architect / Integrated Solutions Architect

U.S. Department of State — Uganda Mission

(2016 – 2021)

Role

Led enterprise architecture and modernization initiatives across mission operational systems. Consolidated fragmented data platforms into unified analytics environments supporting national public health programs, mission leadership reporting, and operational planning.

Key Accomplishments

  • Designed Agile national COVID-19 reporting data warehouse integrating data from 1000+ clinics, national surveillance systems, and border monitoring platforms.
  • Consolidated fragmented operational systems by designing centralized enterprise data architecture enabling mission-wide reporting.
  • Implemented analytics data mart for OBMS (Overseas Business Management System) Motor Pool operations, improving transportation resource planning and operational visibility.
  • Advised national health leadership on scalable data architecture and supervised migration of mission-critical reporting infrastructure.
  • Modernized enterprise collaboration environments through deployment of Microsoft 365 and secure digital collaboration systems.

Senior Database Engineer / Data Warehouse Architect

U.S. Department of State — Uganda Mission

(2003 – 2016)

Role

Designed enterprise database architectures and large-scale data warehousing systems supporting operational intelligence, financial oversight, and program monitoring for major global health initiatives funded through the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR).

Key Accomplishments

  • Designed enterprise data warehouses integrating 50+ partner datasets to improve transparency and reporting accuracy for $2B+ global health funding programs.
  • Built cooperative agreement management reporting systems enabling leadership to track funding allocation and implementation across international partners.
  • Led development of a national blood bank information system supporting 25M+ patient records while managing a software development team.
  • Developed project monitoring databases supporting 50+ health facility construction programs, reducing funding inefficiencies through structured reporting workflows.
  • Introduced early enterprise analytics platforms enabling automated data consolidation and leadership dashboards for program performance monitoring.

Skills

Data Engineering Expertise


Data Platform Engineering

Designing and implementing modern data platforms that support analytics, machine learning, and operational workloads.

Cloud Data Platforms – Snowflake, BigQuery, Redshift, Databricks
75%

Pipeline Orchestration – Airflow, Dagster, Prefect
90%

Distributed Processing – Spark, Flink, scalable batch & streaming pipelines
80%

Data Modeling & Analytics Engineering

Designing scalable enterprise data models that support analytics, reporting, and machine learning workloads.

Dimensional Modeling – Star schemas, Kimball methodology
99%

Enterprise Data Models – normalized models, canonical datasets
99%

Analytics Engineering – dbt transformations, semantic modeling
85%

Business Event Data Modeling (BEAM)

Designing data models around core business events and operational activities to create analytics-ready datasets that accurately reflect how the business operates in real time.

Process-Oriented Data Design – aligned data structures with real business workflows
96%

Semantic Metric Layer – consistent, reusable business metrics
90%

Activity & Event Fact Tables – event streams and foundational activity tables
96%

Academic

Credentials


01

MSc Big Data

University of Sterling, Scotland, United Kingdom
2024

02

BSc (Statistics Major)

Makerere University
2002

Notable Award

Citations


Have any Questions?

Frequently Asked Questions


I specialize in designing scalable data platforms that transform fragmented operational systems into reliable analytics environments. My work often involves integrating multiple enterprise data sources—such as procurement, HR, logistics, financial, and program data—into unified data models that support reporting, analytics, and advanced data science workloads. I focus on building durable pipelines, scalable architectures, and curated datasets so that teams can answer important business questions quickly and confidently.

Reliable pipelines are built using layered architecture and strong data engineering patterns. I typically implement progressive transformations where raw data is ingested first, then refined through curated transformation layers before reaching analytics-ready datasets. This structure improves traceability and simplifies debugging. I also prioritize pipeline observability by implementing monitoring, data validation checks, and alerting mechanisms to ensure pipelines remain dependable even as scale and complexity increase.

My approach begins with understanding the business processes and operational events that generate data. From there I design models that balance analytical performance with long-term maintainability. Depending on the use case, I apply dimensional modeling, event-driven models, or normalized enterprise data models to ensure the resulting datasets are both scalable and easy for analysts and data scientists to use. The goal is to build data models that enable rapid insight without sacrificing data integrity.

I start by identifying the key business questions stakeholders need answered and then work backward to determine the data required to support those insights. During discovery sessions I explore topics such as data sources, expected query patterns, latency requirements, and downstream consumers. This approach ensures that the resulting data platform supports real analytical needs rather than simply moving data between systems without delivering meaningful value.

I work across both traditional enterprise data systems and modern cloud-based data platforms. My experience includes relational warehouses such as PostgreSQL and SQL Server, analytics engineering frameworks like dbt, and orchestration systems such as Apache Airflow. I am particularly interested in designing the architecture that integrates these tools into cohesive data platforms that support engineers, analysts, and machine learning teams simultaneously.

I design systems that separate ingestion, transformation, and consumption layers so each part of the platform can evolve independently. I also emphasize modular transformations, documentation, and reusable data models. This approach allows teams to expand their analytics capabilities without creating fragile systems that break as complexity increases.

A successful self-serve data platform requires curated datasets, consistent data models, and clear documentation. My goal is to create a semantic layer that allows analysts and data scientists to explore trusted data without needing to understand the underlying pipeline complexity. This usually involves building standardized transformation layers, well-defined metrics, and discoverable datasets so teams can focus on analysis instead of data preparation.

Data warehouse entropy occurs when pipelines and tables grow without structure or governance. I mitigate this by enforcing layered modeling patterns, standard naming conventions, and curated transformation workflows. Over time this creates a clean architecture where raw data, transformed data, and analytics-ready datasets are clearly separated, making the platform easier to maintain and extend.

Data quality is best addressed through automated validation embedded directly into the pipeline lifecycle. I implement checks that validate schema integrity, expected record counts, and key business rules before data reaches analytics layers. Combined with monitoring and alerting, this ensures that data issues are detected early and resolved before they impact reporting or decision making.

What excites me most is the opportunity to build systems that empower organizations to make better decisions with data. Well-designed data platforms unlock insights that were previously hidden across disconnected systems. I enjoy architecting those platforms—combining scalable infrastructure, clean data models, and reliable pipelines—so that data becomes a strategic asset for the organization.