Role
- Looker BI Senior Analyst Shift
- 2.00pm
- 11.00pm

About the Role
We are looking for a skilled and hands-on Looker BI Senior Analyst to join our analytics team. You will own the design, development and governance of our Google Looker semantic layer, and help power a natural-language, conversational analytics experience that lets business teams get reliable answers directly from data. This is a senior, hands-on role that blends deep LookML and semantic-layer expertise with strong stakeholder management, as you help expand our reporting capabilities, onboard new data sources, and support data quality and validation best practices across the platform. Required Skills & Qualifications Total Exp
- 7+ years, Looker Exp
- 5+ years
- Robust, hands-on domain expertise in Google Looker, including LookML modeling, Explores, Views, derived tables, and dashboard development
- Significant hands-on experience with semantic-layer tools and implementations (Looker or comparable platforms such as dbt Semantic Layer, Cube, or AtScale)
- Proven experience designing scalable, reusable data models and governance standards within a semantic layer
- Strong SQL skills, with the ability to write and optimize complex queries across large datasets
- Strong experience and expertise with cloud data warehouse technologies, especially BigQuery
- Experience configuring or validating AI-driven / conversational (natural-language) analytics tools built on top of a BI semantic layer
- Working knowledge of Git-based version control and deployment/UAT-to-production workflows for BI or semantic-layer platforms
- Experience partnering with business stakeholders across multiple personas to gather requirements and deliver self-service analytics
- Strong communication and stakeholder management skills, with experience presenting to both technical and business audiences
- Experience developing and maintaining technical documentation and training/enablement materials
- Senior-level experience in BI/analytics engineering roles, with demonstrated ownership of end-to-end reporting solutions
- Comfortable working within a client's existing, approved tooling and platform constraints, and partnering closely with a separate data engineering team that owns upstream data preparation

Key Responsibilities
1. Semantic Layer & LookML Development
- Design, build, and maintain LookML data models, Explores, Views, and derived tables in Google Looker




- Improve and strengthen LookML relationships, measures, dimensions, and descriptions to support a reliable semantic foundation
- Own and evolve the semantic layer to ensure consistent, reusable business logic across dashboards, reports, and natural-language queries
- Define and enforce naming conventions, governance standards, and documentation for the semantic layer
- Collaborate with data engineering to align semantic-layer design with upstream data source structures 2. Conversational Analytics & AI Agent Support
- Guide and support configuration of natural-language, conversational analytics agents built on top of the Looker semantic layer
- Guide and support configuration of natural-language, conversational analytics agents built on top of the Looker semantic layer
- Advise on single-agent vs. multiple-agent approaches based on usability, coverage, and existing platform capabilities
- Validate agent-generated answers for accuracy and reliability against agreed, business-approved reference results
- Identify, document, and help resolve questions the implementation cannot yet answer reliably
- Support Git integration, deployment workflows, and UAT/production environment setup for the semantic layer and agent configuration 3. Reporting, Dashboards & Analytics Delivery
- Build and maintain Looker dashboards, Looks, and Explores that support core business reporting across the organization
- Partner with business stakeholders across multiple personas (for example, editorial, product, and marketing) to translate reporting requirements into scalable Looker deliverables
- Expand the range of personas and delivery audiences supported by the analytics platform, including new user groups, use cases, and self-service content
- Present analytical findings and dashboard walkthroughs to both technical and non-technical stakeholders 4.
Data Quality, QA & Validation Support
- Support and help expedite QA and validation efforts for broadly used, business-critical data to improve accuracy, consistency,



and trust in reporting outputs
- Prepare and execute business validation against agreed reference results; investigate and help resolve discrepancies
- Contribute to test plans, data validation checks, and reconciliation processes across Looker models and source data
- Partner with data engineering to triage and help resolve data quality issues identified during validation 5. Agent Training & Best Practices Enablement
- Advise on and support agent-training and validation best practices on an ongoing basis
- Create and maintain documentation, training materials, and enablement content for internal teams and business users
- Support onboarding and upskilling of team members and stakeholders on Looker and semantic-layer usage 6. Data Source Onboarding & Semantic-Layer Roadmap
- Help accelerate onboarding of additional data sources into the Looker semantic layer, including integrating and validating joins and metrics for newly added sources
- Support semantic-layer roadmap development and planning, including prioritization of new models, data sources, and capabilities
- Partner with data engineering and architecture teams to assess new data sources for integration feasibility and modeling approach 7. Delivery, Documentation & Stakeholder Collaboration
- Maintain a shared, prioritized backlog with the client's technical team and support business validation at agreed checkpoints
- Participate in regular progress reviews and demonstrations of completed work
- Proactively escalate source-data, access, and scope dependencies early
- Produce clear documentation, UAT support, and user guidance to support release readiness and handover Nice to Have
- Experience with QA / data validation frameworks or data engineering practices (pipeline testing, reconciliation, automated validation)
- Familiarity with agent-training or AI/ML model validation workflows
- Experience onboarding new data sources into a BI or semantic-layer platform
- Experience with additional cloud data warehouse platforms (e.g., Snowflake, Redshift)
- Experience with ETL/ELT tools and data workflow orchestrators (e.g., Airflow, dbt, Fivetran)
- Domain experience within editorial and/or marketing functions (e.g., content performance, audience engagement, and campaign reporting)
- Prior experience in a client-facing or consulting environment supporting multiple stakeholder groups

📌 Looker BI Senior Analyst (India)
🏢 Trantor
📍 India