Data Platform Engineering — ETL Pipelines & Analytics
Your Data Exists. Now Make It Usable.
Most organizations have more data than they can use — distributed across operational systems, files, logs, and databases that were never designed to work together. We build the platforms that bring that data together: ETL pipelines that move it reliably, warehouses that store it in a queryable structure, visualization layers that make it legible to the people who need it, and governance frameworks that ensure the data is accurate, discoverable, and compliant.
BigQueryLookerLooker StudioDataflowdbtCloud ComposerAirflowCloud SQLData CatalogDataplexETLELTData WarehouseData Lake
/Offerings
BigQuery
Primary analytical warehouse platform
Looker
Primary visualization and semantic modeling platform
GCP-native
All platform components run on Google Cloud
/What we do
Data Platform Engineering — ETL Pipelines & Analytics
/Approach
How we deliver this service.
01
Data Landscape Assessment
Source system inventory, data volume and velocity analysis, current reporting and analytics gaps, and data quality problem identification. We document what data exists, where it lives, and what the business needs from it before any platform design begins.
02
Platform Architecture Design
Data warehouse design (BigQuery), pipeline architecture (Dataflow/Composer), visualization layer design (Looker/Looker Studio), and governance framework design (Data Catalog/Dataplex) — documented before implementation begins.
03
Pipeline and Warehouse Implementation
ETL pipelines built against the architecture, BigQuery datasets and tables provisioned, data transformations implemented in dbt, and orchestration DAGs deployed in Cloud Composer.
FAQ
Frequently asked questions
What does a modern data platform on Google Cloud include?
A modern data platform on Google Cloud typically includes: an ingestion layer (Pub/Sub, Dataflow, or batch pipelines), an ETL/ELT transformation layer (dbt, Dataflow, or Cloud Data Fusion), a data warehouse (BigQuery) for scalable analytical storage, a visualisation layer (Looker or Looker Studio), a data quality framework enforcing validation and monitoring, and a data catalog (Dataplex) for governed data discovery. QueuesHub engineers each of these layers and integrates them into a cohesive, production-grade data platform.
Why is data quality management important for analytics platforms?
Data quality management is essential because analytics, dashboards, and AI models are only as reliable as the data they consume. Without enforced quality rules, organisations discover errors only after decisions have been made on flawed data. QueuesHub's data quality services implement automated validation checks at each pipeline stage — covering schema conformance, completeness, freshness, referential integrity, and statistical anomaly detection — so that every data asset reaching the analytics layer is verified and trusted.
/Related
Other disciplines in this practice.
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Data Lineage
Data Quality
CDC
Data Without a Platform Is Just Storage
Every organization generates data. The ERP records every transaction. The CRM logs every customer interaction. The mobile application tracks every user session. The production system generates operational metrics every second. But data sitting in disconnected operational systems is not a business asset — it is a liability. It is inaccessible to the people making decisions, inconsistent across systems, and impossible to audit.
A data management platform changes that. It creates a structured layer between raw data and business decisions: pipelines that move data reliably, a warehouse that stores it in an analytically queryable form, a visualization layer that turns queries into insights, and governance mechanisms that ensure the data is trustworthy.
We build data platforms on GCP's native data services — BigQuery as the primary warehouse, Looker and Looker Studio for visualization, Dataflow and Cloud Composer for pipeline orchestration, and Data Catalog and Dataplex for governance.
Our Five Data Management Services
Data Transformation & ETL — Building the pipelines that extract data from source systems, transform it to the target schema, and load it reliably into the data warehouse or data lake. Batch, streaming, and CDC-based approaches depending on the latency requirements.
Data Visualization — Designing and implementing the reporting and dashboard layer: semantic models in Looker, dashboards in Looker Studio, and visualization standards that translate business questions into consistent, maintainable reports.
Data Warehouse Architecture — Designing the BigQuery environment: dataset structure, table partitioning and clustering, schema design for analytical queries, access control architecture, and cost management for warehouse operations.
Data Quality — Implementing the validation, monitoring, and alerting layer that ensures the data moving through the platform is accurate, complete, and consistent — before it reaches the visualization layer and informs business decisions.
Data Catalog — Building the metadata and discoverability layer using GCP Data Catalog and Dataplex: asset registration, tagging, lineage tracking, and the governance model that makes data discoverable and auditable across the organization.
Capabilities
BigQuery architecture: dataset structure, partitioning, clustering, access control
ETL/ELT pipeline development: batch, streaming, and CDC-based
dbt data transformation model development
Cloud Composer (Airflow) DAG development and pipeline orchestration
Looker semantic model (LookML) development
Looker Studio and Looker dashboard design and implementation
Data quality framework: validation rules, monitoring, alerting
Data Catalog asset registration, tagging, and lineage tracking
Dataplex data governance and data zone configuration
Data warehouse cost management: query optimization, slot management
Data lake architecture: Cloud Storage organization, partitioning, lifecycle
Real-time data ingestion: Pub/Sub, Dataflow streaming pipelines
04
Visualization and Semantic Layer
LookML models built against the warehouse schema, dashboards and reports implemented against the agreed requirements, and data quality monitoring activated.
05
Governance and Handover
Data catalog populated, lineage tracking configured, access control policies applied, and the platform handed over with operational documentation and a data team knowledge transfer.