Former Microsoft data architects working with Los Angeles and Southern California enterprise organisations across media, technology, healthcare, finance, and retail. Azure data platform design, governance, semantic layer architecture, and AI-ready data foundations.
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Los Angeles is home to one of the most diverse enterprise landscapes of any major US market — media and streaming companies with audience data at global scale, technology startups and mid-market software firms with product analytics challenges, healthcare networks with multi-system integration complexity, and financial services firms with strict governance requirements.
The common pattern across LA organisations: data infrastructure built for an earlier version of the business, never redesigned as scale and complexity grew. The result is high maintenance burden, analytics environments analysts don't trust, and AI initiatives that stall because the data foundation is not ready. We design and build the architecture that resolves this — with the rigour of a firm that came from inside the platforms, not a generalised consultancy that adds Tableau or Azure to its service list.
Services
Structured review of your current data environment — platform, pipelines, governance, data quality, and BI layer. Identifies root causes of performance, reliability, and cost problems. Produces a prioritised roadmap. Fixed-scope, $15,000–$40,000.
End-to-end design and delivery of Azure data platforms. Medallion architecture on ADLS, Databricks, dbt, Synapse, or Fabric. We also work with GCP BigQuery and Snowflake architectures common in the LA tech market.
Data governance frameworks using Unity Catalog on Databricks, Azure Purview, or equivalent. Access control design, data lineage, policy enforcement, and documentation standards.
Canonical metric definition and semantic layer design for organisations where the same number appears differently in different reports. Works with Tableau, Power BI, Looker, and custom BI stacks.
Data architecture designed to support AI and ML workloads — data quality frameworks, feature store design, and retrieval infrastructure for RAG-based applications.
Migration from legacy on-premise data warehouses (SQL Server, Teradata, Oracle) to modern cloud-native platforms with minimal disruption to existing analytics operations.
Common Questions
LA enterprises span very different industries — media/streaming, technology, healthcare, finance, retail — and the data architecture problems reflect that diversity. Media companies typically struggle with large-scale event data pipelines that were built for scale but never for governance. Tech companies have data sprawl from rapid growth — dozens of data sources, no single source of truth, metric definitions that vary by team. Healthcare organisations deal with multi-system integration and access control complexity. Finance and PE firms need reliable audit trails and strict access segregation. The common thread is that the architecture was built for the organisation's earlier scale and has never been redesigned.
Yes. Media and streaming is a sector we know well. The data architecture challenges in content and streaming companies are distinctive: large-scale audience event pipelines, content recommendation infrastructure, complex advertising data systems, and the need to reconcile content performance data across first-party and third-party measurement sources. We have worked with media and content organisations on both their analytical data platforms and the real-time infrastructure that feeds them.
A data architecture assessment (identifying current-state problems, mapping root causes, producing a prioritised roadmap) typically runs $15,000–$40,000 for a mid-market LA organisation. A full data platform build on Azure, Snowflake, or Databricks runs $80,000–$260,000 depending on scope. Ongoing architecture advisory typically runs $8,000–$20,000 per month. We provide fixed-price proposals after a no-cost discovery call.
Yes. While our background is deep in the Microsoft Azure and Databricks ecosystem, we work across cloud platforms including GCP and Snowflake. Many LA tech companies are on GCP or have multi-cloud architectures. The core data architecture principles — medallion architecture, semantic layer design, governance frameworks, observability — are platform-agnostic. We can advise and deliver across Azure, GCP, and AWS environments.
We run AI data readiness assessments that identify specifically what is blocking production AI in your organisation. The most common gaps are data quality (AI systems produce unreliable outputs when trained or queried against poor quality data), lack of a governed semantic layer (AI agents need reliable, consistent metric definitions), and retrieval infrastructure (RAG-based applications need vectorised, well-organised content). We identify exactly what is missing and build the foundation that makes AI viable at the production level.
Yes. Unity Catalog on Databricks is a governance architecture we work with regularly. We design and implement Unity Catalog rollouts covering metastore structure, catalog and schema organisation, access control policies, data lineage, and the dbt transformation layer that sits above it. We can also advise on how Unity Catalog fits within a broader Azure + Databricks + Power BI or Tableau architecture.
Data Architecture Consulting Across the US & Australia
Book a 30-minute scoping call. We will assess your data environment and tell you exactly what is causing the problems you are experiencing.
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