Senior Analytics Consultant Looker BI

About the Role

We are looking for a Senior Consultant with deep, hands-on Looker (LookML) expertise to work on enterprise BI engagements. A typical project may be a fresh Looker development build or a migration of existing reports from other platforms into Looker — in both cases delivered on a governed cloud data warehouse with row-level security.

This is a developer-heavy consulting role: you will design semantic models, validate data before building, work day-to-day with Data Engineering teams, review peers code, and communicate directly with clients. We are specifically looking for someone eager to upskill into adjacent and emerging technologies — Omni in particular — and who treats learning new tools as part of the job, not an interruption to it.

Key Responsibilities

• Design and build Looker semantic models end-to-end: model files, views, explores, LookML dashboards, and Looks.
• Migrate legacy reports into Looker: analyze the source report, map its columns to warehouse tables, identify and document gaps, and rebuild it to standard — not pixel-copy it blindly.
• Validate data before building: profile source tables in SQL for completeness, coverage, and quality so dashboards are built on verified data rather than assumptions.
• Implement and maintain row-level security using Looker user attributes and access filters.
• Collaborate with Data Engineering: propose column mappings, raise precise data gaps and blockers (which table, which column, what it blocks), and secure their sign-off before building — push calculations upstream to the warehouse rather than hiding logic in LookML.
• Work in Lookers Git-based development workflow, including peer review of teammates LookML.
• Establish and enforce LookML standards across a team (naming, model structure, shared view libraries, review checklists).
• Communicate with client stakeholders: demo work, explain trade-offs, flag risks early, and keep delivery visible sprint over sprint.
• Mentor junior developers and review their work.

Must-Have Skills

Looker / LookML (core requirement)

• Expert-level LookML: views, explores, model files, dimension/measure design, dimension groups, drill fields.
• Scalable model architecture: reusable base explores and explore inheritance, shared views defined once and used across models, a clean include structure, and curated field exposure rather than surfacing everything.
• Row-level security: knows how to implement RLS in Looker using user attributes and access filters.
• Liquid templating: parameters, templated filters, and conditional logic for dynamic report behavior.
• Caching and performance: datagroups and caching policies, persistent derived tables and when not to use them, and query performance tuning.
• Looker content management: LookML dashboards vs UI dashboards, folder/permission structure, embedding basics.

SQL & Data Warehousing

• Strong SQL — data profiling, joins, and window functions; the habit of proving that data exists, is populated, and carries real signal before building on it.
• Dimensional modeling literacy: star schemas, fact/dimension design, slowly changing dimensions and point-in-time joins, and recognizing and preventing join fan-out.
• Comfortable working against a cloud data warehouse and building reports only on governed, modeled tables.

Migration Experience

• Delivered at least one BI migration: legacy reports (proprietary platforms, Tableau, SSRS, or similar) rebuilt in Looker against a new data model.
• Comfortable with the reality of migration work: source-to-target mappings that turn out inaccurate, test and production data that differ, incomplete or poor-quality data — and the discipline to verify, document, and escalate rather than silently work around it.

Consulting & Communication

• Clear written and verbal communication with clients and cross-functional teams (data engineering, product, program management).
• Able to state blockers precisely and distinguish hard blockers from degradations; comfortable saying "this cant be built yet, and here is exactly why."
• Self-directed in ambiguous, fast-changing project environments.

Good to Have

• Omni — strongly preferred. Omnis modeling layer is closely related to LookML, so strong Looker skills transfer directly; it is the intended upskilling path for this role, and prior exposure is a significant plus.
• Other BI platforms: Tableau, Power BI, Sigma — breadth across tools indicates the adaptability we need.
• Git/GitLab fluency: branches, merge requests, peer review, resolving conflicts — Looker (and Omni) development is version-controlled.
• Embedded analytics delivery: signed/SSO embedding, white-labeled reporting.
• Looker API / SDK, System Activity, or admin experience (users, roles, user-attribute sync).
• dbt or similar transformation-layer exposure.
• AI-assisted development experience (e.g., Claude, Copilot) — our teams actively use AI tooling in delivery.
• Hands-on Snowflake experience; SnowPro certification is a plus.
• Looker (LookML Developer) certification.

What We Value in This Role

• Upskilling mindset. The explicit plan for this position is to grow from Looker into Omni and other emerging BI technologies. We want someone who has done this kind of jump before and enjoys it.
• Verification over assumption. The best Looker developers here check the data before they build the tile.
• Team standards over personal style. Shared view libraries and model conventions only work when everyone follows them; additive, review-friendly changes are the norm.
• Ownership. Seniors here review peers code, deploy to production, unblock juniors, and face the client.