Google Cloud Data Engineering Services
Pipelines, lakes and warehouses on Google Cloud, secured and automated.
Stack we work in
- Apache Airflow
- Apache Spark
dbt- Terraform
- GitHub Actions
- Python
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your Google Cloud data platform needs work
We design, build and run data platforms on Google Cloud.
Read the full overview
Our Google Cloud data engineering services cover BigQuery data warehousing, batch and streaming pipelines with Dataflow (Apache Beam) and Pub/Sub, orchestration with Cloud Composer (Apache Airflow), Spark workloads on Dataproc and data lakes on Cloud Storage. We replicate operational databases such as Cloud SQL into BigQuery with Datastream, model the data with dbt, and govern it with Dataplex and IAM. Reports run in Looker or Looker Studio, and Vertex AI can use the same governed data for machine learning. Everything is defined in Terraform and deployed through CI/CD, with monitoring and cost controls from the start.
Most Google Cloud data estates grow one query at a time. These are the symptoms of growth without a plan.
Scheduled queries everywhere
Transformations live in dozens of BigQuery scheduled queries with no version control, tests or dependencies.
Downloaded service account keys
Pipelines authenticate with JSON key files stored on servers or in repositories.
Tables without partitions
Large tables are scanned in full for every query, so costs grow with every new dashboard.
Full reloads every night
Every table is copied in full because there is no change data capture.
One project for everything
Development, test and production share a project, so a test job can break a live report.
Outcomes
What you get
- Dataflow and Datastream pipelines that load only new and changed data
- A BigQuery warehouse partitioned, clustered and modeled for analytics
- Access through IAM groups and service accounts, with no keys in code
- Infrastructure in Terraform and pipelines deployed through CI/CD
- BigQuery costs that are visible, budgeted and predictable
What we deliver
What you receive from a Google Cloud engagement
Data platform foundation
Projects per environment, BigQuery datasets per layer, Cloud Storage buckets, naming and labels aligned with your organization.
Configuration-driven ingestion
Dataflow and Datastream pipelines where adding a source means adding configuration, not writing new code.
Access and encryption
IAM groups, service accounts, VPC Service Controls, Cloud KMS keys and Secret Manager for every credential.
Tested transformations
dbt or Dataform models with tests and documentation, scheduled by Cloud Composer.
Monitoring and budgets
Cloud Monitoring alerts for failed runs and late data, plus budgets and query quotas per project.
Platform documentation
Architecture diagrams, data flows and runbooks your operations team can use on day one.
Google Cloud data platform
- BigQuery data warehouse design and development
- Data lakes on Cloud Storage with BigLake tables
- Cloud SQL and operational database integration
- Dataplex catalog, lineage and data quality
Built with
- BigQuery
- BigLake
- Cloud Storage
- Dataplex
- Cloud SQL
- SQL
Pipelines & orchestration
- Dataflow (Apache Beam) batch and streaming pipelines
- Event ingestion with Pub/Sub
- Change data capture with Datastream
- Cloud Composer (Apache Airflow) orchestration
- Spark and Hadoop workloads on Dataproc
- dbt transformations in BigQuery
Built with
- BigQuery
- Dataflow
- Apache Beam
- Pub/Sub
- Cloud Composer
- Apache Airflow
- Dataproc
- Datastream
Analytics, security & DevOps
- Looker and Looker Studio reporting
- Vertex AI and BigQuery ML on governed data
- IAM, VPC Service Controls and Cloud KMS
- Terraform and CI/CD for data pipelines
- Migration to BigQuery from on-premises and other clouds
Built with
- BigQuery
- BigQuery ML
- Looker
- Looker Studio
- Vertex AI
- Terraform
- IAM
- VPC Service Controls
- 01StoreWarehouse and lakehouse
- BigQuery
- BigQuery ML
- Apache Iceberg
- 02TransformModels and tests
- Apache Spark
- dbt
- Python
- 03OrchestrateScheduling, CI/CD, catalog
- Apache Airflow
- Terraform
- 04ServeBI, semantic layer, activation
- Looker
- Looker Studio
Why teams choose Equitysoft for Google Cloud data engineering
BigQuery costs you can predict
Partitioning, clustering and budgets are part of the design, so query spend is visible and bounded from the first release.
No downloaded keys
Access runs through IAM groups and service accounts, with secrets in Secret Manager, so there are no keys in code.
Infrastructure as code
Terraform and CI/CD for every pipeline and dataset, so environments match and changes are reviewed.
Inside your organization policies
We follow your resource hierarchy, shared VPC and labels, and raise network changes in week one. 50+ engineers, 20+ of them certified on cloud and data platforms, delivering since 2018.
How we work
Assess
We map sources, existing pipelines, projects, BigQuery usage and security requirements.
You receive Google Cloud data platform assessment
Design
Datasets and layers, partitioning, pipeline tools and the project and access model are agreed up front.
You receive Google Cloud data architecture and security design
Secure
We set up service accounts, IAM groups, VPC Service Controls, Cloud KMS and Secret Manager before any production data arrives.
You receive Identity, network and encryption design
Build
Dataflow, Datastream and dbt pipelines are built as code and parameterized for each environment.
You receive Parameterized pipelines in source control
Automate
Releases run through CI/CD; Cloud Composer schedules, Cloud Monitoring alerts and budgets are configured.
You receive CI/CD pipelines, alerts and dashboards
Migrate and support
We move remaining workloads to BigQuery and support the platform in production.
You receive Migration plan and runbooks
Tech stack
Technologies we use
AI and machine learning
Data platforms
Pipelines and orchestration
- Apache Airflow
- Apache Spark
- dbt
BI and analytics
Languages
Cloud and DevOps
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Composer, Dataproc, Cloud Storage, Dataplex, Datastream, Cloud SQL, Looker, Looker Studio and Vertex AI, with dbt and Terraform.
SQL in BigQuery, usually through dbt or Dataform, covers most batch transformations. Dataflow suits streaming and complex event processing. Dataproc suits existing Spark or Hadoop jobs you want to move without a rewrite. We pick the simplest option that meets your latency and volume.
Yes. Datastream replicates changes from MySQL, PostgreSQL, Oracle and SQL Server into BigQuery, and we use incremental dbt models and MERGE statements so each run processes only new and changed data.
Pipelines run as service accounts with only the roles they need, and external systems use Workload Identity Federation instead of downloaded keys. Remaining secrets live in Secret Manager.
Yes. Events arrive through Pub/Sub and are processed in Dataflow, or written straight to BigQuery with a BigQuery subscription when no transformation is needed. The choice depends on latency and how much processing the data needs.
Yes. We move data from on-premises databases and other cloud warehouses, translate SQL with the BigQuery Migration Service where it helps, rebuild pipelines and reconcile results before cut-over.
Partitioned and clustered tables with required partition filters, the right choice between on-demand and capacity pricing, query quotas, materialized views for repeated queries, Cloud Storage lifecycle rules and budget alerts per project.
Yes. We follow your resource hierarchy, organization policies, shared VPC, naming and labels, and request changes through your normal approval process.
Looker Studio suits quick, low-cost dashboards. Looker adds a governed semantic model in LookML, so metrics are defined once and reused across reports. Many teams start with Looker Studio and move to Looker as the number of users and metrics grows.
Yes. BigQuery ML trains common models with SQL inside BigQuery, and Vertex AI covers custom training, feature management and model serving on the same governed tables.
An assessment takes a few weeks. The first pipelines and BigQuery layers usually follow soon after, and further sources are added in waves. Organization policy and network changes often set the pace, so we raise them in week one.
It depends on scope. Defined work such as an assessment, a BigQuery foundation or a first set of pipelines can be fixed price, and ongoing platform work suits time and materials or dedicated engineers. Google Cloud usage is billed to your own billing account. We send a written estimate with its assumptions after a first call. As a guide, an assessment usually takes 2 to 3 weeks and costs $3,000 to $10,000, and a first production phase takes 6 to 10 weeks and costs $15,000 to $60,000 (USD).
More about our Google Cloud Data Engineering
Working togetherHow we work with your team
Inside your organization policies
We work within your resource hierarchy, organization policies and change process, not around them.
Network and security teams early
VPC Service Controls and firewall rules often have the longest lead time, so we raise them in week one.
Infrastructure as code
Every project, dataset and permission is defined in Terraform your cloud team can review like any other change.
Analysts in the loop
Models and metric definitions are reviewed with the people who use the reports, before they go live.
Where Google Cloud data engineering fits best
Retail and ecommerce
Orders, web events and marketing data joined in BigQuery for sales, stock and campaign reporting.
Healthcare
Encryption at rest and in transit, access reviews and data kept in the Google Cloud regions you choose, in line with HIPAA, GDPR and local privacy rules.
Logistics
Shipment and telematics events through Pub/Sub and Dataflow for near-real-time status reporting.
SaaS
Product usage events and tenant-separated datasets that load each customer's data without mixing it with another's.
Talk to a Google Cloud data engineer
Tell us about your Google Cloud projects and what you need your pipelines to do.