Data Services

Data Warehouse Development Services Built to Scale With Your Questions

Cloud and lakehouse architecture on Snowflake, BigQuery, Redshift, and Databricks - modeled for the decisions you have not asked yet.

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Overview

Warehouse architecture modeled for the questions you have not asked yet

Our data warehouse development services cover architecture design, cloud warehouse build, schema and metadata modeling, pipeline integration, legacy migration, and performance tuning. We ship on Snowflake, Google BigQuery, Amazon Redshift, and Databricks Delta Lake - picked to fit your stack and budget, not ours. Whether you need a cloud data warehouse, a data lakehouse architecture, or a modern data architecture for scalable data storage, the build is modeled around your decisions, with metadata management, legacy database migration, and performance optimization baked in.

Years in business: 12
Team members: 65+
Global clients: 30+
Yr avg. client retention: 4+

Who this is for

Mid-market businesses with data sitting in 5+ source systems and no single source of truth.
Teams running into the limits of an OLTP database used for analytics.
Organizations on a legacy on-prem warehouse planning a cloud migration.
Subscription, eCommerce, or SaaS businesses that need cohort, LTV, and churn analytics current dashboards cannot answer.

What you get

Architecture document - source inventory, target schema, fact and dimension model, refresh cadence, retention policy.
Cloud warehouse build - Snowflake, BigQuery, Redshift, or Databricks Delta Lake, set up with role-based access, resource monitors, and cost guardrails.
Schema, fact, and dimension tables - modeled around the actual analytical questions the team needs answered.
Metadata management and data dictionary - every field defined, owned, and discoverable.
Performance optimization - clustering, partitioning, materialized views, query optimization, tuned against real workload.
Migration playbook - data parity tests, cutover steps, and rollback paths when moving off a legacy warehouse.

How we work

01 Step

Audit

We map sources, downstream consumers, SLAs, and the questions analytics is being asked. Predefined success metrics are set in audit.
02 Step

Plan

We pick the warehouse, model the schema, and document refresh cadence and access controls.
03 Step

Build

We stand up the warehouse, ingestion pipelines, and modeling layer in increments - each increment signed off against a defined use case.
04 Step

Test

Data parity tests, validation scripts, performance benchmarks, and stakeholder UAT before each rollout.
05 Step

Scale

Optimize queries, layer governance, and extend the model as new sources come on.

Tools & stacks we use

The platforms our team is fluent in for this practice. Most engagements span a few of these, picked for the actual problem rather than for the demo.

Snowflake
Google BigQuery
Amazon Redshift
Databricks Delta Lake
Fivetran
Stitch Data
Celigo
Azure Data Factory
dbt Cloud
Apache Airflow

Need dedicated experts?

Hire a specialist embedded with your team

Pre-vetted senior talent for this practice - hourly, retainer, dedicated FTE, or Micro-GCC. Vetted in 48 hours, managed end-to-end by H4H operations.

Data Engineer Hire a Data Engineer Snowflake Developer Hire a Snowflake Developer

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Frequently asked questions

Still have a question? Talk to a real human on our team - we usually reply within one business day.

Talk to a specialist

What is a data warehouse and do I need one?

A data warehouse is an analytical store optimized for asking questions across many sources at once. You need one when reports take hours to refresh, when finance numbers disagree with marketing numbers, or when no one can confidently answer "what is our LTV by acquisition channel."

Which warehouse should I pick - Snowflake, BigQuery, Redshift, or Databricks?

It depends on existing cloud commitments, workload type, and team skills. Snowflake for pure analytics with mixed workloads. BigQuery for GCP-native and pay-per-query economics. Redshift for AWS-deep stacks. Databricks for analytics plus ML on the same platform.

How long does a data warehouse build take?

6–14 weeks for a typical mid-market build, depending on source count and model complexity. Migrations from legacy on-prem stacks run longer.

How much do data warehouse development services cost?

Project-based engagements typically start in the mid-five figures and scale with source count and team size. Retainer and dedicated FTE models are quoted in monthly bands.

What results can I expect?

Faster, trustworthy analytics. We have shipped warehouses that drove company-wide adoption (LegalZoom), enabled forecasting models with ~90% accuracy (Bouqs), and supported 24-hour client onboarding dashboards (D.Luxury Brands).

Can you migrate us off a legacy warehouse without downtime?

Yes. We run a parallel-run pattern - the new warehouse runs alongside legacy, data parity is validated, then cutover happens at a controlled moment.

How is H4H different from a Snowflake reseller or implementation partner?

We are not a reseller. We pick the warehouse that fits - sometimes that is Snowflake, sometimes it is not. We also own the layers above and below the warehouse, not just the warehouse install.

Will my team be able to maintain it after handoff?

Yes. We document architecture, schema, pipelines, and runbooks. Most clients keep us on for strategic extensions while in-house owns daily ops.

Proof points

Related case studies

What we have shipped for clients with adjacent problems. Each one is sourced and attributable.

LegalZoom - Professional Services / LegalTech

Company-wide adoption from execs to department leads

Built data warehouse, pipelines, and a Tableau visualization layer balancing performance with drill-down to grain-level data.

Read case study

CandyClub - D2C / Subscription

Subscription data architecture enabling rotation, churn, and product-mix analytics

End-to-end ingestion, validation, and modeling for a curated subscription brand. Pipelines surfaced rotation, churn, and product-mix patterns the prior stack could not.

Read case study

Related services

Ready to put your data to work?

Book a free audit and we will map the problem, the metrics, and the smallest first build that proves value.

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