04Data Management Platforms

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 LakeData LineageData QualityCDC
/Offerings
4.1

Data That Moves When the Business Needs It.

ETL pipelines are the circulatory system of a data platform. When they are reliable, the data warehouse is current, the dashboards are accurate, and the business can make decisions on fresh data. When they break silently, decisions get made on stale or incomplete data — without anyone knowing. We build ETL and ELT pipelines that are observable, testable, and maintainable: designed to fail loudly and recover cleanly.

4.2

Data Your Team Can Actually Read.

A data warehouse full of accurate data delivers no value until the people who need it can see it, interact with it, and act on it. We design and implement the visualization layer that sits between the warehouse and the business: semantic models that translate technical data structures into business concepts, and dashboards that answer the questions decision-makers actually ask — without requiring them to write SQL.

4.3

Design the Warehouse Before You Fill It.

A data warehouse is only as good as its architecture. Tables with the wrong granularity, missing partitioning on columns that are always filtered, joins across datasets with no clustering, and no access control model — these are architectural decisions that are expensive to undo once the warehouse is in production and pipelines are loading data into it. We design BigQuery architectures that perform, scale, and remain maintainable as the data volume and query complexity grow.

4.4

Decisions Made on Bad Data Are Worse Than No Decision.

Data quality is the validation, monitoring, and alerting layer that stands between your data pipelines and your business decisions. Without it, bad data flows silently through the warehouse and into dashboards where it gets treated as fact. We implement data quality frameworks that catch data problems at the point of ingestion, flag anomalies before they reach reporting, and give data engineers the observability they need to maintain data platform health over time.

4.5

Know What Data You Have. Know What It Means.

As data platforms grow, the challenge shifts from having too little data to knowing what data you have. A data catalog is the governance layer that makes data discoverable, understood, and trustworthy at scale. We implement data catalogs on GCP using Cloud Data Catalog and Dataplex — registering data assets, defining business glossaries, enforcing classification policies, and building the lineage tracking that lets any data consumer trace where a metric came from and who owns it.

BigQuery
Primary analytical warehouse platform
Looker
Primary visualization and semantic modeling platform
GCP-native
All platform components run on Google Cloud
/What we do

Your Data Exists. Now Make It Usable.

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
/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.

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.

/Related

Other disciplines in this practice.

Cloud Engineering

Cloud Infrastructure Engineered to Perform.

Software Development

Software Built to Your Specification.

Enterprise Integration

Your Systems Should Talk to Each Other.

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