Cloud & Data Platform Architecture

Complex data systems, made clear and durable.

Bradallc helps organizations design the full path from operational data to trusted reporting—relational databases, data warehouses, lakehouses, ETL and ELT pipelines, and analytical delivery.

My work sits at the intersection of hands-on engineering and architectural clarity. The goal is not just to move data, but to build systems that are understandable, scalable, and genuinely useful to the people who rely on them.

That means translating complex business requirements into elegant solutions—models that hold up over time, pipelines that can be trusted, and reporting that reflects the business clearly.

Explore services Selected experience

Strong data platforms are built end to end: clear models, reliable movement of data, and reporting that stays connected to business reality.

Services

Relational database development

Design and development of operational databases with a focus on normalization, performance, maintainability, and durable application support.

Data warehouses and lakehouses

Architectures built for analytics, including dimensional models, star schemas, snowflake schemas, curated warehouse layers, and modern lakehouse patterns.

ETL and ELT pipelines

Batch and incremental pipelines to ingest, transform, unify, and deliver data across enterprise systems and analytical environments.

Reporting and analytics enablement

Semantic models, reporting structures, and Power BI solutions that connect well-modeled data to decision-making.

What Brada brings

Deep experience in data warehousing and modernization projects across Oil & Gas, Insurance, and Higher Education. The common thread is taking fragmented, aging, or overly complex environments and turning them into systems that are clearer, more discoverable, and easier to operate.

The work covers relational and dimensional modeling, warehouse design, data integration, governance-minded architecture, and the analytical layer that business users actually see.

This is end-to-end data engineering: modeling, designing, moving data, transforming it, and delivering it through reporting.

Snowflake Azure Synapse Microsoft Fabric Databricks Palantir Power BI ADF Qlik

End-to-end approach

Good platforms are not assembled as isolated pieces. They are designed as a coherent flow from source systems to trusted consumption.

1. Model Understand operational structures, business entities, and analytical needs.

2. Design Build relational, dimensional, and lakehouse-ready structures that can endure change.

3. Move Create ETL and ELT pipelines that reliably bring data across systems.

4. Curate Shape raw data into governed, usable, analytics-ready assets.

5. Deliver Expose the results through semantic models, reporting, and business-facing analytics.

Architecture philosophy

Tools matter, but structure matters more. Durable systems come from good modeling, careful thinking about how data changes over time, and a clear line between operational reality and analytical representation.

The best solutions are not the most complicated ones. They are the ones that absorb complexity without spreading it everywhere else.

That is where deep analytical skill matters: understanding the business well enough to simplify without losing meaning.

Core capabilities

Relational database design for operational systems
Dimensional modeling for reporting and analytics
Star and snowflake schema design
Warehouse and lakehouse architecture
ETL and ELT pipeline development
Power BI data modeling and reporting enablement
Platform modernization and migration planning
Translation of complex business requirements into durable technical solutions

Selected experience

Western Midstream — Architecting and developing a core data platform: models for Power BI, Azure Data Factory and Qlik pipelines, integrations from systems such as Intelex and Ariba into Synapse, governance-oriented practices, architected and led the migration strategy from Synapse to Microsoft Fabric and OneLake.

Upstream Data Foundation at Exxon — Architected Snowflake ingestion pipelines, implemented entity unification with Tamr, and supported Golden Records feeding EBX MDM to establish a trusted data foundation for analytics and governance.

Mainframe to SQL modernization at GMAC — Designed the target schema, migration strategy, and ETL pipelines to move a large Lender Placed Insurance platform from IDMS and DB2 into SQL Server, enabling a full transition into a modern .NET ecosystem.

Across these efforts, the recurring theme has been modernization with structure: not just replacing technology, but building cleaner foundations that make future work easier.

About

I’m a Cloud & Data Platform Architect with deep experience designing and delivering modern data ecosystems across Oil & Gas, Insurance, and Higher Education.

My work combines hands-on engineering with architectural clarity: building scalable pipelines, designing dimensional models, establishing governance-minded practices, and modernizing enterprise data platforms.

I care about building data systems that are reliable, discoverable, and genuinely useful for the people who depend on them.