Looker Delivery-Verified Partner
Google Cloud Premier Partner
AI / ML Specialization
Get Your Looker Implementation Right First Time.
Most Looker implementations fail not because of the platform, but because of the architecture. SquareShift's senior Looker architects design, build, and govern your semantic layer from the ground up — so your teams get trusted data, fast.
250+
Looker implementations delivered
10+
Years of Looker expertise
6–12
Weeks typical go-live timeline
F500
Enterprise delivery standard
What's in scope
Access Controls & Governance
Row-level, column-level, SSO/IAM
Conversational Analytics Setup
Training & Knowledge Transfer
Developer & end-user enablement
Data Source Integration
BigQuery, Snowflake, dbt, Databricks
Gemini-powered NLP from day one
LookML Model Architecture
Designed for scale, not just day one
THE HIDDEN COST OF GETTING IT DONE
Why Looker Implementations Go Wrong
Common mistakes made by under-resourced teams and generalist SIs — and how SquareShift avoids every one of them from day one.
Poor LookML Architecture
Models built as an afterthought — with no view hierarchy, missing primary keys, or incorrectly defined join relationships — produce wrong numbers and become impossible to maintain as the org scales.
No Semantic Layer Governance
When every team defines metrics differently — revenue in five flavors, conversion rates that contradict — BI tools create distrust, not clarity. Without a governed semantic layer, Looker becomes just another dashboard tool.
Ignored Access Controls
Row-level and column-level security bolted on after go-live leads to costly rework, compliance risk, and data exposure. Security must be designed into the LookML model from the start — not added as an afterthought.
Performance Bottlenecks
Explores built on raw or staging tables, missing PDT strategies, over-reliance on runtime calculations — these kill dashboard performance and erode user adoption. Query optimization must be baked into the architecture.
Zero User Adoption
A technically excellent Looker deployment fails commercially if end users don't know how to use it. Without structured onboarding and co-development sessions, your BI investment sits idle — used only by a small data team.
DIY Warehouse Without dbt
Connecting Looker directly to unstaged warehouse tables — without a dbt transformation layer — means business logic is scattered across LookML, SQL, and spreadsheets. The modern stack (BigQuery + dbt + Looker) exists for good reason.
SquareShift addresses every one of these risks through our architecture-first implementation methodology — designing the LookML model, data governance rules, and warehouse integration strategy before writing a single line of code.
EVERYTHING YOU NEED
What's Included in Every Implementation
SquareShift's Looker implementation is a complete end-to-end engagement — not a partial setup that leaves your team to figure out the rest.
LookML Model Design & Development
We architect and build your LookML semantic model from the ground up — views, explores, dimensions, measures, and derived tables — using best practices that keep the model scalable and maintainable as your data grows.
Data Source & Warehouse Integration
We configure and optimize Looker's connection to your data warehouse — BigQuery, Snowflake, Databricks, or Redshift — and align the LookML model on top of your dbt mart layer for clean, performant querying.
Access Controls & Data Governance
Row-level security, column-level access, user attribute–based filtering, and SSO via Google Cloud IAM or your identity provider — all configured within the LookML model for robust, auditable governance.
Views & Explores
PDTs
Datagroups
Big Query
Snowflake
DBT
Databricks
Row-Level Security
SSO
Cloud IAM
Conversational Analytics & Agentic BI
We configure Looker's Conversational Analytics capabilities — powered by Gemini — so your business users can query your governed data in plain language from day one. For advanced requirements, we integrate the Conversational Analytics API for custom agentic workflows.
Dashboard & Explore Development
We build your initial set of governed dashboards and explores — covering your priority business questions — with consistent formatting, drill paths, performance-optimized caching, and mobile-responsive layouts.
Training & Knowledge Transfer
We run structured co-development sessions with your internal Looker analysts so they own and can extend the model post-handoff. End-user onboarding ensures your business teams are self-sufficient from go-live — no ongoing analyst dependency.
Gemini
NLP Querying
Agentic BI
Dashboards
Explores
Drill Paths
Developer Training
End-User Sessions
Documentation
DATA STACK EXPERTISE
Warehouses & Tools We Work With
BigQuery
Primary specialization
Snowflake
Full support
dbt Core / Cloud
Upstream transformation
Databricks
SQL & ML workloads
Redshift
AWS environments
OUR METHODOLOGY
The SquareShift Looker Implementation Process
A structured, five-phase approach that delivers a governed, scalable Looker environment — without shortcuts that create future technical debt.
Phase 1
1–2 weeks
Discovery & Data Stack Audit
We begin every engagement with a deep requirements session — understanding your data warehouse schema, business questions, team structure, existing analytics, and reporting pain points. If you have existing LookML, we audit it fully and surface technical debt before writing a single line of new code.
DELIVERABLES
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Data stack audit report with findings and recommendations
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Business question inventory and priority matrix
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Refactor vs. rebuild recommendation (for existing LookML)
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Project scope, timeline, and milestone plan
Phase 2
1–2 weeks
Architecture & Semantic Layer Design
We design the full LookML architecture before building it. This includes deciding which explores to create, how to structure views and derived tables, how to handle fanout and symmetric aggregation, and how to align the semantic model with your warehouse schema and dbt mart layers.
DELIVERABLES
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LookML model architecture document
-
Explore and view structure diagram
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Data governance and access control design
-
Performance and caching strategy (datagroups, PDTs)
Phase 3
4–12 weeks
Build, Configure & Integrate
Core explores, dimensions, measures, and initial dashboards are built with best practices from the start — no shortcuts that create maintenance debt. Data source connections, access controls, and row-level security are configured alongside the model, not bolted on afterward. For GCP environments, we configure SSO via Cloud IAM, BigQuery optimization, and private networking.
DELIVERABLES
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Production-ready LookML codebase in version control
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Governed explores with row-level and column-level security
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Priority dashboards and scheduled reports
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Conversational Analytics and Gemini integration (where applicable)
Phase 4
1–2 weeks
Testing, Training & Go-Live
We validate data accuracy end-to-end — reconciling Looker output against warehouse queries and source systems. Co-development sessions with your internal Looker analysts ensure they can own and extend the model post-handoff. End-user onboarding sessions ensure your business teams get value from Looker on day one, not day 30.
DELIVERABLES
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Data validation and accuracy sign-off report
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Developer co-development sessions (LookML training)
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End-user onboarding workshops
-
Go-live readiness checklist and runbook
Phase 5
Ongoing
Handoff & Post-Launch Support
You receive complete documentation of the model architecture, a codebase walkthrough, and access to our post-launch support retainer. Most clients transition to SquareShift's ongoing Looker support tier — giving your team a senior escalation layer as your data needs evolve, new data sources are added, and your user base grows.
DELIVERABLES
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Full model architecture documentation
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Codebase walkthrough and handoff session
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Looker health check framework
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Optional ongoing support retainer
WHO WE SERVE
Looker Implementations Across Every Industry
We've delivered Looker for organizations across financial services, SaaS, retail, and healthcare — from early-stage startups to global enterprises.
SaaS & Technology
Product analytics, customer health scoring, usage metering, and churn prediction — built on Looker's semantic layer for self-serve insights across product, CS, and finance teams.
Financial Services
Transaction analytics, regulatory reporting, risk dashboards, and portfolio performance tracking — with row-level security and column-level access controls built for compliance from day one.
Retail & eCommerce
Revenue attribution, inventory optimization, customer segmentation, and marketing performance analytics — with a single Looker semantic layer trusted across commercial, supply chain, and finance teams.
Healthcare & Life Sciences
Clinical operations, claims analytics, provider performance, and patient outcome reporting — built with the governance and security controls required for HIPAA-aligned data environments.
Migrations That Delivered
"From day one, SquareShift's architects hit the ground running. Their ability to design a clean, scalable LookML model — and get our analysts up to speed without hand-holding — was exactly what we needed. We went live in 9 weeks."
"We had inherited LookML that no one could understand. SquareShift audited it, recommended a clean rebuild, and delivered a model where our team of 6 analysts went from a weeks-long backlog to same-day reporting. The governance alone was worth it."
Rahul K, Head of Data, Series B SaaS Company
Anjali M, VP Analytics, Retail Enterprise
WHO WE SERVE
Ready to Build Your Looker Foundation?
SquareShift is a Delivery-Verified Google Cloud Premier Partner. Tell us about your data stack — we'll scope your implementation, give you a clear timeline, and show you exactly what's possible.
Free Discovery Call
30 minutes with a senior Looker architect. We'll assess your data stack and give you a no-obligation scope estimate.
LookML Audit
Already have Looker deployed? We'll audit your existing LookML, identify technical debt, and give you a clear improvement roadmap.
Migration Assessment
Moving from Tableau, Qlik, or Domo to Looker? We'll design a migration plan with minimal business disruption and maximum carry-over.
