Google Cloud data engineering

GCP data engineering services and BigQuery consulting

SquareShift provides GCP data engineering services. We design, migrate, modernize, and operate Google Cloud data platforms for new programs and programs already in motion. You get trusted metrics, explainable cost, migration continuity, governed BI, and AI-ready data products.

Talk to a data platform expert

Or see the delivery plan for new, in-flight, and migration programs.

Credentials and proof

Google Cloud and Looker credentials, plus proof of delivery.

SquareShift is a Google Cloud Premier Partner with Data Analytics, Machine Learning and Generative AI specializations, and a Looker Consulting Partner. Read about our Google Cloud partnership.

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BI credentials

100+

BI instances we have worked on.

26,000+

BI and Looker assets migrated across SquareShift delivery work.

Fortune 500

Delivery work across Fortune 500 accounts.

Migration performance

4x

Faster BI migrations than manual migration.

12 weeks

Average migration timeline, down from 12 months.

98%+

Migration consistency during validation.

GCP data engineering services we deliver

Our data engineering services in GCP cover six layers: ETL and ELT, data lakes, data products, analytics and machine learning, business intelligence, and AI. For the Looker and BI layer, see Google Cloud business intelligence.

Data programs stall when leaders cannot see who owns the data, the cost, the logic, or the risk.

The cause is rarely one weak cloud service. It is usually a gap between how fast the platform grows and the operating model around it.

  1. Metrics are unclear.

    Teams define the same number in different places, so leaders stop trusting reports.

  2. Pipeline ownership is weak.

    Jobs run until they break. Then alerts, runbooks, retries, and owners are hard to find.

  3. BigQuery cost is hard to explain.

    Storage, query patterns, dashboard load, and slot choices are not tied to owners.

  4. Migration risk is underplanned.

    Legacy logic, reports, access, cutover, and rollback need more than a move plan.

  5. AI demand is ahead of the data.

    AI teams need curated, governed, documented data before Vertex AI work can scale.

  1. Consistent metrics

    Looker semantic modeling and governed definitions keep reports aligned.

  2. Owned pipelines

    Alerts, runbooks, retries, quality checks, and a named owner for every job.

  3. Explainable cost

    Usage, query, dashboard, storage, and ownership signals are visible.

  4. Migration continuity

    Legacy logic, reports, validation, cutover, and hypercare stay controlled.

  5. AI-ready data

    Curated, governed, observable data ready for BigQuery ML and Vertex AI.

The so what

Data stops being a cost leaders tolerate and becomes an asset they can fund, govern, and scale.

BigQuery consulting: design, cost control and performance

SquareShift's BigQuery consulting covers warehouse design, query patterns, storage design, dashboard load and workload management, so every cost has an owner. Read our BigQuery cost and performance guide, or see the BigQuery optimization case study.

For the product itself, see the BigQuery documentation from Google Cloud.

Delivery plan

SquareShift takes data programs from any starting point to a plan leaders can fund, govern, and track.

It works for new programs, programs already underway, migration, modernization, and operating support. The output is a funded backlog, a control plan, and a 30/60/90-day execution path.

01

Assess

Map systems, data, pipelines, dashboards, access, cost, governance, and risk.

What we review

  • Source and target inventory
  • BigQuery cost baseline
  • Looker and dashboards
  • Governance and access
02

Decide

Choose what moves, what is rebuilt, what retires, and where logic lives.

Architecture decisions

  • BigQuery design choices
  • Dataform model placement
  • Looker semantic boundaries
  • Dataplex and Knowledge Catalog needs
03

Plan

Turn decisions into workstreams, owners, validation rules, waves, and release controls.

Control plan

  • Risk register
  • Validation plan
  • Cutover and rollback plan
  • Executive checkpoints
04

Mobilize

Stand up the squad, cadence, environments, code review, testing, and governance.

Delivery start

  • 30/60/90-day plan
  • Delivery squad
  • Release cadence
  • Platform health checks

Platform depth

Google Cloud data engineering across every layer of the data stack

One team covers every layer, from the data foundation to AI. We build on Google Cloud core services and add the open source tools that make them stronger.

Layer Google Cloud core Complementary toolsOpen source and ecosystem Outcome
Layer 05

AI-ready data products

Curated data paths for models, embeddings, agents, and serving.

  • Vertex AI
  • Gemini
  • BigQuery vector search
  • Vertex AI Vector Search
  • Vertex AI Agent Builder
  • LangChain
  • LlamaIndex
  • Agent Development Kit
  • Hugging Face
  • Model Context Protocol

Governed data that production AI and agents can use.

Layer 04

BI and semantic layer

Define metrics once, expose them safely, and support self-service.

  • Looker
  • LookML
  • Looker Studio
  • Gemini in Looker
  • Embedded analytics
  • Spectacles
  • Looker SDKs
  • Henry
  • Gazer
  • Connectors for Tableau, Power BI, Sheets

One definition for every metric, in every tool people already use.

Layer 03

Analytics and ML engineering

Tested models, notebooks, and ML workflows that are repeatable.

  • Dataform
  • BigQuery ML
  • BigQuery DataFrames
  • Colab Enterprise
  • Vertex AI Workbench
  • Vertex AI Pipelines
  • dbt Core
  • Jupyter
  • scikit-learn
  • PyTorch
  • TensorFlow
  • MLflow
  • Kubeflow Pipelines
  • SQLFluff
  • Great Expectations

Models and ML workflows you can test, version, and release.

Layer 02

Pipelines and orchestration

Move, stream, schedule, and retry data, with an owner for every failure.

  • Dataflow
  • Pub/Sub
  • Cloud Composer
  • Datastream
  • Dataproc
  • Managed Service for Apache Kafka
  • Apache Beam
  • Apache Airflow
  • Apache Spark
  • Apache Kafka
  • Debezium
  • Airbyte

Every pipeline has an owner and a failure path.

Layer 01

Data foundation

The warehouse, lake, and access base for the whole platform.

  • BigQuery
  • Cloud Storage
  • BigLake
  • Dataplex
  • IAM
  • Apache Iceberg
  • Apache Parquet
  • Avro
  • Terraform

An open, secure base the whole platform can trust.

Governance and BigQuery cost control are built into every layer.

Every type of data platform project
  • Greenfield builds
  • In-flight programs
  • Migrations
  • BI modernization
  • Cost optimization
  • Governance
  • Operations
Migration moves through all five layers at once.

We migrate one business slice at a time, from source data to adoption, instead of one layer at a time.

See the migration method ↓

Migration and modernization

SquareShift’s iterative delivery framework ships business outcomes every release, drastically accelerates migration, and de-risks the move to Google Cloud.

We start with serious discovery. Then we shape the migration into business-specific releases that move the full data chain together, from source data to the people who use it.

Traditional Data Platform Migration Approach

ETL, storage, summaries, semantic model, BI, and adoption move in separate waves. This suits platform groundwork. Working business outputs arrive near the end.

Our Iterative Delivery Approach

Each slice moves from source data to adoption as one release, with validation, observability, stakeholder feedback, and support built in.

02 / Delivery methodology

How it works: map the estate, plan the slices, then deliver them one at a time.

The assessment and roadmap produce a list of releases. Only one or two are in build at once, and each one ships to production with evidence.

  1. 01

    Map the estate

    Assess systems, data flows, logic, reports, owners, access, cost drivers, and dependencies.

  2. 02

    Build the iterative roadmap

    Turn the assessment into a prioritized list of business slices. Each slice is one release.

  3. 03

    Deliver iteratively

    Build one or two slices at a time across the full data chain, from source to BI.

  4. 04

    Release with evidence

    Validate, reconcile, review with stakeholders, put into production, and support users.

Steps 01–02

Slice roadmap

4 slices planned
  • Slice 06 · Finance close packExecutive reporting
  • Slice 07 · Supply chain domainDepartment domain
  • Slice 08 · Customer 360Data product
  • Slice 09 · Margin analysisBusiness capability
Step 03

In delivery

2 slices in build
  • Slice 04 · Sales pipelineSemantic layer · stage 6 of 10
  • Slice 05 · Inventory domainPipelines · stage 3 of 10
Step 04

Released

3 slices live
  • Slice 01 · Revenue reportingLive · validated
  • Slice 02 · Finance domainLive · validated
  • Slice 03 · Orders data productLive · validated

What each release teaches feeds back into the roadmap.

03 / Slice types

Four common ways to select and shape a slice.

Slices follow how the business works. Each one is useful on its own the day it is released.

Slice 01

Business capability

Move one decision workflow end to end, from source data to a trusted report or data product.

Slice 02

Department data domain

Move one team’s core data domain with ownership, validation rules, access, and support.

Slice 03

Operational data product

Move a production data product with pipelines, curated outputs, monitoring, and release controls.

Slice 04

Executive reporting pack

Move a high-trust reporting set with metric definitions, dashboard validation, and adoption support.

Benefits of the iterative delivery framework

  • Lower delivery risk

    Issues surface inside one slice, not across the whole estate.

  • Faster time to market

    Working outputs reach users in weeks, not at the end of the program.

  • Less business team time

    Stakeholders review one slice at a time, not every layer at once.

  • Change course between iterations

    Reprioritize the roadmap as business needs shift.

  • Scale delivery up or down

    Add or reduce squads to change speed without replanning.

  • Cost effective

    Spend tracks delivered value, release by release.

  • Lighter governance load

    Fewer changes in flight means simpler reviews and approvals.

  • Visible progress for leaders

    Every release is a checkpoint leaders can see and fund against.

The result is a controlled move to Google Cloud, with useful releases, clear progress, strong validation, and business value visible earlier.

Move data to BigQuery

See how we handled a Snowflake to BigQuery migration. For the wider move to Google Cloud, see our Google Cloud migration services.

Four engagements

Solutions we’ve crafted for our clients.

All case studies
01 / Technology Dashboards Refresh in Ten Seconds, Not Hours, for a Recruiting Platform Read → 02 / Technology Analytics Cost Cut for a Global Learning and Talent Software Provider Read → 03 / Technology Learning Progress in Real Time for a Global EdTech Platform Read → 04 / Education Analytics 40% Cheaper and Hours Faster for an EdTech Platform Read →

Questions about GCP data engineering

What does a GCP data engineer do?

A GCP data engineer builds and runs the pipelines, warehouses and data products on Google Cloud. In our delivery, every pipeline has an owner, alerts, runbooks, retries and quality checks.

What is BigQuery used for?

BigQuery is the warehouse and data lake base of a Google Cloud data platform. It sits in the data foundation layer next to Cloud Storage and BigLake, and BigQuery ML builds models on top of it.

How do I reduce BigQuery costs?

Tie storage, query patterns, dashboard load and slot choices to owners. Start with a BigQuery cost baseline, then optimize. See the BigQuery optimization case study.

How long does a Snowflake to BigQuery migration take?

About 4 weeks for a proof of concept, and 18 to 30 weeks for the migration. A migration assessment scopes the move first, and each release ships working outputs in weeks, not at the end of the program.