Data Warehouse

AIGard designs and builds data warehouses that give your organization one trusted source for analytics. We handle modeling, loading, performance and cost.

The work typically covers:

• Warehouse architecture and data modeling
• Source system integration
• Transformation layers
• Access control and governance
• Query performance and cost tuning
• Migration from legacy warehouses

Data Warehouse is usually the right investment in one or more of these situations:

• Teams argue about whose numbers are right
• Analytics queries slow down operational systems
• You're moving off an on-premise warehouse
• Data is scattered across many tools

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

The choice between a data warehouse and a lakehouse depends on your data types and workloads. AIGard recommends based on what you will actually query and how.

We examine your source systems and the analytics your teams need during scoping, then set out the recommendation in a written proposal. We work with platforms including Snowflake, BigQuery and Databricks, and where a legacy warehouse exists we review it before proposing a migration.

AIGard controls data warehouse cost through data modeling, clustering, workload management and monitoring of expensive queries.

Cost is considered alongside performance from the design stage, since the way data is modeled and loaded affects what each query consumes. Query performance and cost tuning then addresses the queries that monitoring shows to be most expensive.

With the decisions the business needs to make, then the data required to support them.

We assess your sources, quality and existing platforms first, and tell you plainly what your data can and cannot support before recommending an architecture.

PII masking, encryption and audit logging are built into data work, and engineers operate inside your environment under least-privilege access.

Data-handling requirements are agreed during scoping and checked before release.

For Data Warehouse, AIGard engineers typically work with Snowflake, BigQuery, Databricks, dbt, Airflow and PostgreSQL.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

Data Warehouse is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.