Looker partner, validated by Google Cloud for implementation quality.
Looker Delivery-Verified Partner
Looker consulting services: get your implementation right first time
Looker consulting from SquareShift, a Delivery-Verified Looker partner validated by Google Cloud. Our senior Looker architects design, build and govern your LookML semantic layer, then migrate, embed and support Looker after go-live.
- Access Controls & GovernanceRow-level, column-level, SSO/IAM
- Data Source IntegrationBigQuery, Snowflake, dbt, Databricks
- LookML Model ArchitectureDesigned for scale, not just day one
- Conversational Analytics SetupGemini-powered NLP from day one
- Training & Knowledge TransferDeveloper & end-user enablement
250+Looker implementations delivered
10+Years of Looker expertise
6–12Weeks typical go-live timeline
F500Enterprise delivery standard
Credentials and proof
A Looker consulting partner on Google Cloud, Delivery-Verified for implementation quality.
Partner credentials
Google Cloud Premier Partner.
AI / ML Specialization.
Analyst recognition
Google Looker is recognized as a Leader in Analytics & BI Platforms. SquareShift is a Delivery-Verified partner validated by Google Cloud for implementation quality. Source: Gartner Magic Quadrant, 2025.
Work With UsWork with Looker experts, not generalists
A senior Looker consultant works with your team on LookML, governance and performance. SquareShift has delivered 250+ Looker implementations.
The hidden cost of getting it done
Why Looker implementations go wrong.
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.
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 MethodologyDesigning the LookML model, data governance rules, and warehouse integration strategy before writing a single line of code.
Looker implementation services: what's included in every project
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.
- 01Views & Explores
- 02PDTs
- 03Datagroups
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.
- 01BigQuery
- 02Snowflake
- 03dbt
- 04Databricks
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.
- 01Row-Level Security
- 02SSO
- 03Cloud IAM
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.
- 01Dashboards
- 02Explores
- 03Drill Paths
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.
- 01Gemini
- 02NLP Querying
- 03Agentic BI
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.
- 01Developer Training
- 02End-User Sessions
- 03Documentation
Data stack expertise
Warehouses & tools we work with.
- BigQueryPrimary specialization
- SnowflakeFull support
- dbt Core / CloudUpstream transformation
- DatabricksSQL & ML workloads
- RedshiftAWS 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.
-
Discovery & Data Stack Audit
1–2 weeksWe 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- Data stack audit report with findings and recommendations
- Business question inventory and priority matrix
- Refactor vs. rebuild recommendation (for existing LookML)
- Project scope, timeline, and milestone plan
-
Architecture & Semantic Layer Design
1–2 weeksWe design the full LookML architecture before building it — 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- LookML model architecture document
- Explore and view structure diagram
- Data governance and access control design
- Performance and caching strategy (datagroups, PDTs)
-
Build, Configure & Integrate
4–8 weeksCore explores, dimensions, measures, and initial dashboards are built with best practices from the start. 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- Production-ready LookML codebase in version control
- Governed explores with row-level and column-level security
- Priority dashboards and scheduled reports
- Conversational Analytics and Gemini integration (where applicable)
-
Testing, Training & Go-Live
1–2 weeksWe validate data accuracy end-to-end — reconciling Looker output against warehouse queries and source systems. Co-development sessions ensure your internal Looker analysts can own and extend the model, and end-user onboarding ensures business teams get value from Looker on day one, not day 30.
Deliverables- Data validation and accuracy sign-off report
- Developer co-development sessions (LookML training)
- End-user onboarding workshops
- Go-live readiness checklist and runbook
-
Handoff & Post-Launch Support
OngoingYou 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 — a senior escalation layer as your data needs evolve, new data sources are added, and your user base grows.
Deliverables- Full model architecture documentation
- Codebase walkthrough and handoff session
- Looker health check framework
- 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.
01
SaaS & Technology
Built on Looker's semantic layer for self-serve insights across product, CS, and finance teams.
- Product analytics
- Customer health scoring
- Usage metering
- Churn prediction
02
Financial Services
With row-level security and column-level access controls built for compliance from day one.
- Transaction analytics
- Regulatory reporting
- Risk dashboards
- Portfolio performance tracking
03
Retail & eCommerce
With a single Looker semantic layer trusted across commercial, supply chain, and finance teams.
- Revenue attribution
- Inventory optimization
- Customer segmentation
- Marketing performance analytics
04
Healthcare & Life Sciences
Built with the governance and security controls required for HIPAA-aligned data environments.
- Clinical operations
- Claims analytics
- Provider performance
- Patient outcome reporting
05
Manufacturing
Vision and sensor models running where the work happens, one analytics layer across plants, and service workflows that skip the triage queue entirely.
- Quality ML
- Plant analytics
- Predictive maintenance
- OT / IT convergence
06
Lodging & REITs
Portfolio, booking and property-operations data joined into a single governed layer — so asset managers, revenue teams and investors read the same numbers.
- Portfolio data platforms
- Revenue management
- Guest & listing search
- Investor reporting
Looker Delivery-Verified Partner · Google Cloud Premier Partner · AI / ML Specialization
Contact UsFour engagements
Solutions we’ve crafted for our clients.
01 / Technology
Learning Progress in Real Time for a Global EdTech Platform
Read →
02 / Technology
Sales Teams Run Their Own Reports at a US Semiconductor Maker
Read →
03 / Technology
Dashboards Refresh in Ten Seconds, Not Hours, for a Recruiting Platform
Read →
04 / Education
Daily Reporting Unified for a Learning Platform's 1.5 Million Learners
Read →
Learning Progress in Real Time for a Global EdTech Platform
Read →
02 / Technology
Sales Teams Run Their Own Reports at a US Semiconductor Maker
Read →
03 / Technology
Dashboards Refresh in Ten Seconds, Not Hours, for a Recruiting Platform
Read →
04 / Education
Daily Reporting Unified for a Learning Platform's 1.5 Million Learners
Read →
Ways to start
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.
01
Free Discovery Call
−30 minutes with a senior Looker architect. We'll assess your data stack and give you a no-obligation scope estimate.
30 minutes · no-obligation scope estimate
Book Your Discovery Call →02
LookML Audit
+Already have Looker deployed? We'll audit your existing LookML, identify technical debt, and give you a clear improvement roadmap.
Clear improvement roadmap
Book Your Discovery Call →03
Migration Assessment
+Moving from Tableau, Qlik, or Domo to Looker? We'll design a migration plan with minimal business disruption and maximum carry-over.
Tableau, Qlik, or Domo to Looker
Book Your Discovery Call →04
Ongoing Looker Support
+Already live? Our ongoing Looker support tier gives your team a senior escalation layer for performance optimization, new data integrations, LookML model evolution, and Looker health checks.
Optional ongoing support retainer
Book Your Discovery Call →Common questions
How long does a Looker implementation take?
A standard first-time Looker implementation typically takes 6 to 12 weeks from discovery to go-live. Projects involving complex data stacks, multiple warehouses, or migration from another BI tool usually run 12 to 20 weeks. SquareShift provides a precise timeline and milestone plan after the initial discovery session — so you know exactly what to expect before committing.
What does a Looker implementation include?
A complete SquareShift Looker implementation includes: requirements discovery and data stack audit; LookML semantic model architecture and development; data source integration (BigQuery, Snowflake, dbt, Databricks); row-level and column-level access controls; Conversational Analytics and Gemini setup; dashboard and explore development; developer and end-user training; and full architecture documentation at handoff.
What do your Looker consulting services include beyond implementation?
Our Looker consulting services cover everything from implementation and LookML data modeling to embedded analytics, dashboard development, BI migrations, and ongoing support. We help businesses use Looker effectively by aligning analytics with real business goals, not just reporting needs.
Can you work with our existing LookML code?
Yes. We begin with a full LookML audit to assess technical debt, model structure, performance bottlenecks, and governance gaps. Based on the audit, we make a clear, evidence-backed recommendation: refactor what's there, or rebuild cleanly. For most inherited models with significant technical debt, a targeted rebuild produces better long-term results than patching — but we make that call together, with full transparency.
Can you migrate existing BI tools like Tableau or Qlik to Looker?
Yes. We help organizations migrate from Tableau, Qlik, Domo, and MicroStrategy to Looker. Our approach ensures critical dashboards and data logic are retained while improving performance, scalability, and reducing long-term BI costs.
What data warehouses do you support?
SquareShift supports all major data warehouses: Google BigQuery (our primary specialization as a Google Cloud Premier Partner), Snowflake, Databricks, Amazon Redshift, and DuckDB. The majority of our implementations run on BigQuery or Snowflake. We also work extensively with dbt Core and dbt Cloud as the transformation layer upstream of Looker.
Do I need dbt to implement Looker?
Not strictly required, but strongly recommended. The BigQuery + dbt + Looker stack is widely considered industry best practice. dbt handles staging, intermediate, and mart-layer transformations so that Looker's semantic model sits cleanly on well-structured, performant tables — rather than raw warehouse data. SquareShift can design and implement both the dbt transformation layer and the Looker semantic layer together as one cohesive engagement.
Do you offer Looker embedded analytics for applications?
Yes. We build Looker embedded analytics that integrate directly into web applications, portals, and internal systems. This allows users to access real-time insights within their workflows, improving adoption and decision-making.
How do you handle security, performance, and governance in Looker?
We follow Looker best practices for LookML modeling, role-based access control (RBAC), and cloud architecture. Through health checks and performance optimization, we ensure analytics remain secure, scalable, and reliable as data volumes grow.
What is the difference between Looker and Looker Studio?
Looker (formerly Looker Core) is the enterprise BI platform — with LookML semantic modeling, Agentic BI, Conversational Analytics powered by Gemini, API-first architecture, embedded analytics, and row-level governance. Looker Studio (formerly Google Data Studio) is a free, lightweight reporting and visualization tool suited for ad hoc reports. SquareShift specializes exclusively in the enterprise Looker platform.
What happens after the implementation is complete?
Post go-live, SquareShift offers ongoing support retainers covering administration, performance optimization, cost optimization, new data source integrations, LookML model evolution, Looker health checks, and agentic BI feature rollouts. Most clients transition to our support tier after go-live — it gives your team a senior Looker escalation layer as your data needs grow, your user base expands, and Looker's platform continues to evolve.
Is SquareShift a certified Looker implementation partner?
SquareShift is a Looker Delivery-Verified Partner, validated by Google Cloud for implementation quality. We are also a Google Cloud Premier Partner with an AI / ML Specialization.
Is Looker difficult to learn?
Looker is built so your team can own it. We run co-development sessions with your Looker analysts, developer training in LookML and end-user onboarding, so your team can extend the model after handoff.
Do people still use Looker?
Yes. SquareShift has delivered 250+ Looker implementations and provides ongoing support for teams using it.
What does Looker software do?
Looker is the enterprise BI platform. It brings LookML semantic modeling, Agentic BI, Conversational Analytics powered by Gemini, an API-first design, embedded analytics and row-level governance together.
Is Looker owned by Google?
Yes. Google acquired Looker in 2020, and Looker is part of Google Cloud.