Looker Services

Make Looker the governed analytics layer your business can actually trust and use.

Looker is most valuable when it becomes the governed layer between business questions and trusted data. It is least valuable when it becomes another dashboard tool sitting on top of inconsistent definitions, fragile models, and unclear ownership.

SquareShift helps teams implement, migrate, govern, optimize, and extend Looker so analytics becomes easier to trust, easier to scale, and easier for business users to adopt.

Who this is for

This page is for data leaders, BI teams, analytics engineers, product teams, and business technology leaders who need Looker to support real operating decisions.

Typical triggers include a new Looker rollout, migration from legacy BI, slow or confusing dashboards, inconsistent KPIs, LookML technical debt, embedded analytics needs, or business demand for conversational analytics.

The problems we solve

Looker programs fail when they are treated as dashboard projects. The real work is upstream: data modeling, semantic layer design, access control, performance, adoption, and governance.

We help teams answer the hard questions early. Which metrics must be defined once? Which explores should exist? Which dashboards should be rebuilt, retired, or redesigned? Which users need self-service and which need guided reporting? How should Looker connect to BigQuery, Snowflake, dbt, Databricks, or other data platforms?

What we do

Looker implementation

We design and build Looker environments from the ground up: data source connections, LookML models, explores, dashboards, access controls, development workflow, documentation, and user enablement.

Semantic layer design

We help teams define shared business logic, reduce KPI drift, and make metric ownership explicit. The goal is a governed model that analysts can extend and business users can trust.

BI migration to Looker

We help move reports, dashboards, models, and business logic from Tableau, Cognos, Domo, WebFOCUS, Qlik, Power BI, and other BI stacks into Looker. We prioritize rationalization and validation over one-for-one report duplication.

Embedded analytics

We build embedded analytics experiences for SaaS products, internal portals, partner portals, and customer-facing data products using Looker's APIs and embedding patterns.

Conversational analytics

We help teams expose governed Looker data through natural-language analytics so business users can ask questions without bypassing the semantic layer.

Looker support and optimization

We support existing deployments through model cleanup, performance tuning, dashboard rationalization, access review, cost optimization, and ongoing admin support.

How we engage

We usually start with one of five entry points:

Looker implementation discovery
Looker health check
BI migration assessment
Semantic layer and LookML review
Conversational BI pilot

Each engagement produces a practical plan: what to build, what to migrate, what to retire, what to govern, and what business users should get first.

Proof paths

Relevant SquareShift work includes Looker and GCP modernization for an EdTech platform, sales and training analytics for a semiconductor manufacturer, Domo and Looker analytics for a technology skilling platform, and multiple migration and conversational analytics assets.

The pattern across these projects is consistent: the business value appears when the data model, dashboards, users, and operating process are designed together.

What good looks like

A healthy Looker environment has clear metric ownership, performant models, useful explores, dashboards people actually use, access controls that match business reality, and a path for business users to answer more questions without creating report chaos.

Primary call to action

Book a Looker working session. Bring one painful dashboard, one metric people argue about, or one migration target. We will help decide whether the right first move is implementation, migration, health check, semantic layer cleanup, or conversational BI.