Looker Implementation

Get implementation right the first time with guidance from some of the world's most experienced Looker architects.

Focuses on technical specifications, go-live support, and ensuring smooth data flow for accurate reporting.

Our Services

More self-service

Our implementations empower business users to answer queries independently, accelerating confident decisions without analyst dependency.

Less maintenance

We prioritize simple, scalable LookML models to minimize coding and maintenance, saving time and reducing churn.

Actionable analytics

Our efficient workflows enable fast dashboard creation, reducing stress for time-sensitive analyses and delivering actionable insights quickly.

Want to ace your Looker rollout?

Streamline your Looker implementation with expert technical and organizational guidance from BI veterans and Looker experts.

We've implemented and managed Looker at several businesses including Looker itself. Our technical expertise, commitment to quality, and clear communication deliver tailored Looker environments with minimal complexity.

Our collaborative approach, focus on empowering users, and emphasis on data governance make us your trusted partner for data-driven decision-making and maximizing Looker's value.

Let's talk

Common questions

How long does a typical implementation take?: A standard first implementation takes 6–12 weeks. Projects involving complex data stacks, multiple data sources, or a migration from another BI tool typically run 12–20 weeks. We give you a more precise estimate after the discovery session.
Do you work with existing LookML or start from scratch?: Both. We start with an audit of any existing LookML and make a clear 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.
What data warehouses do you support?: BigQuery, Snowflake, Databricks, Redshift, and DuckDB. The majority of our implementations are on BigQuery or Snowflake. We also work extensively with dbt as a transformation layer upstream of Looker.
What happens after the implementation?: We offer ongoing Looker support retainers and training programs. Most clients transition to our support tier after go-live — it gives your team a senior Looker escalation layer as your data needs evolve and your model grows.

"Spencer and David have been exceptional partners, hitting the ground running from day one. Their deep experience and ability to execute efficiently have made a real difference in our projects. What stands out is their responsiveness and experienced talent. They’re quick to act and deliver solutions that work. We know we can rely on them to get things done."

Daniel McCaffrey

Partner and CDO, Dolabra Digital

Deploy quickly and scale confidently

LookML Design and Development

We design LookML models with business users in mind, anticipating usage patterns and enabling broad data queries with minimal complexity. Our expertise ensures scalable, user-friendly analytics.

Developer and User Training

We'll ensure that technical users within organizations understand the strengths and limitations of LookML, helping you get it right the first time and maintain sustainable coding practices. We strive to maximize self-service adoption by educating end users about the reporting UI.

Data Governance and Compliance

Our Looker implementations ensure data integrity and compliance with robust access controls, including row-level and column-level provisioning, critical for sensitive or regulated data.

Our implementation process

1
Discovery
We start with a deep requirements session — understanding your data stack, business questions, team structure, and existing analytics. We audit any existing LookML and surface technical debt before writing a single line of new code.
2
Architecture
We design the LookML model architecture before building it. That means deciding which explores to build, how to structure views, how to handle fanout, and how to align the model with your warehouse schema and dbt models.
3
Build
Core explores, dimensions, measures, and initial dashboards are built with best practices from the start — no shortcuts that create maintenance debt. Access controls and data governance are configured alongside the model, not bolted on after.
4
Training
We run co-development sessions with your internal Looker analysts so they can own and extend the model after handoff. End-user onboarding ensures your business teams can get value from Looker on day one.
5
Handoff
You receive full documentation of the model architecture, a codebase walkthrough, and an optional ongoing support retainer. Most clients transition to our Looker Support tier to maintain momentum post-launch.

Start your implementation

Who we work with

Healthcare Analytics

We built a claims and provider performance platform for a regional health system, designing a Looker model on top of a Snowflake warehouse that pulled from multiple clinical and administrative data sources. The result was a self-service reporting environment used by operations, finance, and clinical teams — replacing a patchwork of Excel reports and reducing analyst turnaround time from days to minutes.

SaaS & Cybersecurity

A security platform company needed to replace homegrown log analysis dashboards that required engineering involvement for every report. We implemented Looker on top of their BigQuery data warehouse, designed governed explores, and trained their customer success team to build reports independently. Non-technical users went from zero Looker access to self-sufficient in under four weeks.

eCommerce & Retail

We migrated a fast-growing retail brand from Tableau to Looker, auditing and restructuring an inherited LookML model that had accumulated years of technical debt. After the rebuild, average dashboard load times dropped significantly, the data team of eight was unblocked from a weeks-long backlog, and the business had a single trusted source of truth for revenue attribution and inventory reporting.