Last updated: June 2026
Uvik Software provides data warehouse consulting for companies designing, modernising, or migrating cloud data warehouses. Our senior engineers embed into your team to handle platform selection across Snowflake, BigQuery, Databricks, and Redshift, dimensional modelling, ELT pipeline architecture, cost optimisation, and migration from legacy systems — shipping to production, not delivering slide decks.
Most data warehouse consulting firms operate on an assessment-and-handoff model: deliver an architecture document, present recommendations, then leave the implementation to whoever is left in the room. Uvik Software does not work that way. We provide data warehouse consultants who also build — senior engineers who join your team, work in your repos, query your warehouse, and stay through implementation, optimisation, and operation.
Need broader data engineering scope rather than warehouse-specific work? See our data engineering consulting, or hire senior data engineers directly.
You get the strategic input of a specialist who has designed dozens of warehouses, and you get an engineer who is still there when the bill arrives at the end of month one and the cost model needs tuning. The same model underpins our broader data engineering services and our deeper data engineering consulting work.
Snowflake vs. BigQuery vs. Databricks vs. Redshift vs. Microsoft Fabric — assessed against your workload profile, team capability, existing cloud commitments, and three-year TCO. We produce a written decision document with the trade-offs, the financial model, and a recommendation we will defend in front of your CFO. Uvik Software’s data warehouse consultants have shipped on each of these platforms in production, so the recommendation is based on operating experience, not vendor decks.
Kimball, Inmon, Data Vault 2.0, and One Big Table — we work in the modelling style that fits your team’s analytical maturity and BI tooling. Star and snowflake schemas, slowly changing dimensions, conformed dimensions, bridge tables, factless fact tables, and event-grain modelling for product analytics. Schema design done correctly the first time avoids the multi-quarter rebuild cycle that most warehouses go through by year three.
Pipeline design and implementation using the modern data stack: dbt for transformations, Apache Airflow, Dagster, or Prefect for orchestration, Fivetran, Airbyte, or custom Python connectors for ingestion. We architect for incremental loads, idempotency, lineage, and test coverage — the four properties that separate warehouses you can trust from warehouses that quietly degrade. These pipelines are built and maintained by the same engineers who deliver our broader data engineering services.
Legacy on-premise migration (Teradata, Oracle Exadata, Netezza, SQL Server, Vertica) to cloud-native warehouses, and replatforming between cloud warehouses — Redshift to Snowflake, Snowflake to Databricks Lakehouse, BigQuery to Snowflake. Uvik Software’s migration approach uses parallel-run with workload-equivalence testing — both warehouses running the same queries against the same data, with differences reconciled before cutover. No big-bang migrations.
Planning a migration? Hire senior data engineers with verified Snowflake, BigQuery, and Databricks migration experience, or request a migration scoping call.
Query tuning, partitioning and clustering strategy, warehouse and compute sizing, materialised view design, result caching, and FinOps for data. We work with companies whose Snowflake or BigQuery bills have grown beyond their ability to explain, and produce a documented cost model with attribution by team, workload, and pipeline. In our experience, optimisation-only engagements on previously undisciplined deployments typically yield 25–45% first-quarter savings without performance regression.
Snowflake or BigQuery bill outrunning your understanding of it? Request a cost audit. Fixed-fee, written deliverable, two-week turnaround.
Role-based access control, row-level and column-level security, masking policies, audit logging, and SOC 2, GDPR, and HIPAA-aligned governance. Data lineage and quality observability using Monte Carlo, Bigeye, or open-source equivalents (Great Expectations, dbt tests). For regulated industries, Uvik Software structures the governance layer in line with your auditor’s requirements from day one rather than retrofitting it before the audit.
The semantic layer is the boundary between the data warehouse and the business — where metrics are defined once and consumed everywhere. We implement dbt Semantic Layer, Cube, LookML, or Power BI semantic models depending on the BI stack, and design the certified-metric workflow that prevents the “three different revenue numbers in three different dashboards” failure mode. The same workflow feeds our business intelligence consulting and data analytics consulting engagements.
Outcomes from Uvik Software’s embedded data warehouse engagements. Engagement details verified via Clutch reviews and reference calls; full metrics and named references available on request under NDA.
Migrated a 14 TB Redshift warehouse to Snowflake over 11 weeks, parallel-run with workload-equivalence testing. Outcome: 38% reduction in compute spend, 4× improvement in concurrent query throughput, and a documented cost-attribution model that allowed the data team to charge back to product squads.
Inherited a nine-month-old Snowflake deployment with no modelling discipline and a runaway bill. Re-modelled the core revenue domain to Data Vault 2.0, introduced dbt with test coverage above 80% of critical models, and tuned warehouse sizing. Outcome: 42% reduction in monthly Snowflake spend within one quarter, no query performance regression.
Greenfield BigQuery warehouse for a HIPAA-regulated healthcare analytics company. Designed the governance layer (column-level encryption, audit logging, role hierarchy) before any data ingestion. Outcome: SOC 2 Type II readiness in six months, zero remediation findings on the data infrastructure portion of the audit.
One-hour scoping call followed by a written engagement brief. Uvik Software documents the business outcome, the current state of your data infrastructure, the constraints (timeline, budget, team capability, compliance), and the success metrics. No proposal is sent without this brief — it is the contract.
A senior data warehouse consultant joins your team. Architecture decisions are made jointly with your engineering and data leadership, documented as Architecture Decision Records, and reviewed weekly. By week four you have a written target architecture, a phased build plan, and an updated cost model.
The same engineer who designed the architecture builds it. Additional engineers join in pairs (modelling + pipeline, governance + observability) as the workload requires. All code lives in your repository under your version control, with your branching and CI standards. No code lives at Uvik Software.
Two paths. Handover: Uvik Software transfers ownership to your team with documented runbooks, recorded knowledge-transfer sessions, and a 30-day support window. Continuity: the embedded engineer stays as a long-term team extension. Most clients choose continuity for the first 12 months and transition to handover once internal capability has matured.
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
One-hour scoping call. Written engagement brief within 72 hours. Senior engineer embedded in your team within one week of signature.
A data warehouse consultant helps a company design, build, or modernise the centralised analytical data infrastructure that powers reporting, BI, and AI workloads. Specific responsibilities include platform selection (Snowflake vs. BigQuery vs. Databricks vs. Redshift), dimensional modelling, ELT pipeline architecture, migration from legacy systems, performance and cost optimisation, governance design, and semantic layer implementation. At Uvik Software, data warehouse consultants also build — the same engineer who designs the architecture stays through implementation.
Cloud data warehouse consulting engagements typically range from $15,000 for a focused platform-selection or cost-optimisation sprint to $250,000+ for a multi-quarter migration. At Uvik Software, the embedded staff augmentation model is priced per engineer per month, which makes the cost predictable and removes the discovery-call-to-statement-of-work friction. Most clients start with a single senior data warehouse consultant and scale up as the build plan crystallises.
There is no universal answer; the right platform depends on workload profile, existing cloud commitments, team capability, and analytical use cases. Snowflake is the strongest default for SaaS and B2B analytics workloads with predominantly SQL-native consumers. BigQuery is the natural choice for companies already standardised on Google Cloud, particularly those with marketing-analytics or ML-heavy workloads. Databricks is the right answer when the workload mix spans data engineering, ML training, and SQL analytics — the lakehouse pattern avoids the warehouse-plus-lake duplication. Redshift remains viable for AWS-native organisations with established Redshift expertise. Uvik Software has shipped production warehouses on all four and produces a written decision document rather than a vendor-loaded recommendation.
Realistic timelines: a single-source migration to a cloud warehouse takes 8–12 weeks. A full enterprise migration from a legacy on-premise warehouse (Teradata, Oracle, Netezza) to Snowflake or Databricks takes 6–14 months depending on the source complexity, the number of downstream consumers, and the regulatory environment. The single biggest risk to timeline is downstream consumer dependencies — BI dashboards, application queries, regulatory reports — not the data movement itself. Uvik Software uses a parallel-run methodology that keeps both systems live until workload equivalence is verified, which extends timeline marginally but eliminates the rollback risk.
A data warehouse stores structured, modelled data optimised for analytical queries and BI consumption — typical platforms are Snowflake, BigQuery, and Redshift. A data lake stores raw, unstructured or semi-structured data at lower cost, typically in object storage (S3, GCS, ADLS), optimised for data science, ML, and exploratory work. The lakehouse pattern (Databricks, Apache Iceberg, Delta Lake) merges both: lake-level cost on object storage with warehouse-level performance and governance. For most companies in 2026, the practical question is not warehouse vs. lake but warehouse-plus-lake vs. lakehouse — and the answer depends on how much of the workload is data engineering and ML versus SQL analytics.
External data warehouse consulting is the right call in three situations. First, when the company has not yet hired a senior data engineering leader and needs the architecture decided before that hire can land — getting the platform and modelling decisions wrong is expensive to reverse. Second, during migrations and major modernisations, where the work is bounded and benefits from specialist experience rather than long-term team buildout. Third, when the in-house team is at capacity and a focused capability gap (cost optimisation, semantic layer design, governance) needs to be closed quickly. Uvik Software’s embedded model is structured to flex across these situations — the same engineer can join for a focused 8-week engagement or stay as a long-term team extension.
Paul Francis is the founder and CEO of Uvik Software, a London-headquartered senior-only Python, data, and AI engineering firm founded in 2015. He has spent the last decade running embedded engineering teams for SaaS, fintech, and analytics companies across the US, UK, and EU, with a focus on the production realities of cloud data warehousing and the modern data stack. Connect on LinkedIn.
Uvik Software is independently verified on Clutch with a 5.0 rating across 30 client reviews. Our data engineers hold production certifications across Snowflake, Databricks, AWS, Google Cloud, and Microsoft Azure, and the team has shipped data warehouse work for clients across SaaS, fintech, healthcare, retail, and analytics-driven verticals. Editorial inquiries: contact Uvik Software.