Engineering™ · Data & Intelligence

Data Platform

We build the cloud data platform your business needs to operate with reliable, unified intelligence.

Modern Data Stack · BigQuery · Snowflake · Databricks · dbt · Airbyte. Automated ingestion, governed cloud storage, and dimensional modeling to power executive analytics and production AI.

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Scope

Cloud Data Warehouse Setup · Automated ELT Data Pipelines · Dimensional Data Modeling (dbt) · Data Quality Testing Framework

Estimated Timeline

6–10 weeks

Platforms

Google BigQuery · Snowflake · dbt Cloud · Airbyte / Fivetran

Target Fit

Is this for your company?

This service is for you if

✓Business data is fragmented across relational databases, CRMs, ERPs, and disconnected spreadsheets.
✓Heavy analytic queries slow down your live production database and degrade customer web app performance.
✓Your analytics team spends 80% of their time manually cleaning data and only 20% delivering actual insights.
✓You need a robust technical data foundation before training predictive models or rolling out generative AI.

You probably do not need it if

✕You only manage a single monthly spreadsheet and have no plans to ingest multiple transactional data sources.
✕You lack internal data analysts or business intelligence consumers to query and leverage warehouse tables.

Problem Space

What we solve

Universal Business Data Centralization

Scheduled, automated data ingestion from production databases (PostgreSQL/MySQL), external APIs, and SaaS tools.

Decoupled Analytics from Production Systems

Complete isolation of heavy reporting workloads in an elastic, serverless cloud data warehouse with zero live lag.

Clean Modular Modeling with dbt

Version-controlled transformation logic in Git with built-in schema testing, data assertions, and auto-generated docs.

AI & Executive Analytics Readiness

Dimensional schemas optimized simultaneously for real-time BI dashboards and high-throughput ML embedding pipelines.

Engineering Process

How it works

01 — Diagnose

Source System & Data Volume Audit

We audit source databases, evaluate required sync frequencies, map table schemas, and assess transactional load.

02 — Design

Warehouse Layering & Architecture Blueprint

We define the layered data architecture (Raw, Staging, Marts) and design dimensional star schemas.

03 — Build

ELT Pipelines & dbt Transformations

We deploy automated connectors, write modular dbt SQL transformations, and configure data validation tests.

04 — Launch

Orchestration, Reconciliation & Handoff

We automate pipeline scheduling, cross-verify data integrity against source ledgers, and train internal teams.

Deliverables

What we deliver

Upon completion you will have

✓Cloud Data Warehouse fully provisioned inside your corporate cloud account (BigQuery or Snowflake).

✓Automated ELT pipelines running scheduled syncs from all connected operational sources.

✓Version-controlled dbt models complete with automated tests and visual data lineage graphs.

✓Domain-specific Data Marts for Sales, Finance, and Operational leadership.

✓Documented business data dictionary and Git transformation repository.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:6–10 weeks

Weeks 1–2Phase 1

Source Discovery & Dimensional Design

Data source cataloging, access provisioning, cloud sizing, and architectural blueprinting.

Weeks 3–5Phase 2

Warehouse Setup & Raw Ingestion Pipelines

Data warehouse provisioning, Airbyte/Fivetran connector configuration, and initial historical backfills.

Weeks 6–8Phase 3

dbt Dimensional Modeling & Testing Suite

Building analytics marts, deduplicating entity records, writing schema tests, and mapping lineages.

Weeks 9–10Phase 4

Financial Reconciliation & Production Go-Live

Cross-auditing against production source truths, automated scheduling, and analyst team onboarding.

Pricing Guidance

Estimated Investment

Target Investment

USD 6,500

Includes cloud warehouse setup, ingestion pipelines for up to 4 data sources, dbt modeling layers, and data quality tests.

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Real-World Proof

Impact Case Study

From 6 Disconnected Databases to 1 Unified Cloud Warehouse with Real-Time Sync

Initial problem

Transportation and logistics provider with fleet data trapped across 3 local ERPs, a MySQL app, and spreadsheets, taking 10 days at month-end to reconcile fleet margin.

Technical intervention

Engineering of a BigQuery + dbt Data Platform unifying vehicle telematics, ERP billing, and fuel consumption logs.

Outcome achieved

Automated route profitability reports delivered every morning, a 95% reduction in manual data processing time, and an analytics-ready base for predictive maintenance.

Clarifications

Frequently asked questions

What recurring cloud costs will a warehouse like BigQuery or Snowflake create?

In BigQuery, you only pay for storage (roughly USD $20 per terabyte per month) and query compute. For the vast majority of mid-market companies, total monthly cloud infrastructure costs range between USD $40 and $120.

Who has access to our sensitive company data during the project?

The entire infrastructure is deployed inside your corporate GCP or AWS cloud account. We access your environment using temporary Least Privilege IAM credentials and never copy proprietary data to external servers.

Can we connect standard BI visualization tools (Power BI, Looker, Tableau)?

Yes. Structured Data Marts in BigQuery or Snowflake connect seamlessly to Power BI, Looker Studio, Tableau, Metabase, or any SQL-compliant reporting tool.

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