Cloud & Data Platform Architect(Looker/Lookml)

Position: Cloud & Data Platform Architect(Looker/LookLM)



Location: San Ramon, CA (On-Site)



Employment Type: Contract



Job Description-



We run production cloud and data platforms for enterprise clients multi-region GCP estates, BigQuery warehouses feeding hundreds of downstream consumers, and BI layers that executives look at every morning. Those platforms are increasingly not single-cloud. Workloads, identity, and data land in both GCP and Azure, and someone has to own the shape of the whole thing rather than one slice of it.



This is that owner. It is a hands-on architect role, not a slideware role. You will design the landing zones, review the Terraform, sit in the incident call when the warehouse queues, and then present the remediation to a client CIO the same week.



What You Will Own



Platform architecture



Target-state architecture for cloud infrastructure and data platforms across GCP (primary) and Azure (secondary but real)



Landing zone design: org/subscription hierarchy, network topology, connectivity (Interconnect / ExpressRoute), DNS, egress control



Identity and access architecture spanning Google Cloud IAM and Microsoft Entra ID, including federation and workload identity



Cost architecture capacity vs on-demand models, BigQuery editions and slot reservations, Azure reservations, chargeback/showback



Data platform



Warehouse and lakehouse architecture: BigQuery, ADLS Gen2, Synapse or Fabric, Databricks where relevant



Ingestion and orchestration design: Pub/Sub, Dataflow, Datastream, Cloud Composer / Airflow, Azure Data Factory



Transformation layer standards: Dataform and/or dbt repo structure, environments, promotion path, CI gates, testing



Data governance: catalog, lineage, classification, retention, PII handling, and the controls that make a GDPR/DPDP audit uneventful BI and semantic layer



Looker platform ownership: LookML model architecture, Explore design, PDT/aggregate-awareness strategy, performance tuning



Access architecture in Looker model sets, user attributes, row-level security patterns that survive an audit



Instance administration, upgrades, embed and API usage, and migration work off legacy BI tools



Engineering discipline



Infrastructure as code as the only path to production Terraform modules, state strategy, policy-as-code



CI/CD across Cloud Build / GitHub Actions / Azure DevOps



Observability and SLOs for both infrastructure and data (freshness, volume,schema, cost anomalies)



Reliability practice: runbooks, RCA ownership, postmortems that change the design



Client and commercial



Translate a business problem into an architecture and a defensible estimate



Lead technical discovery, write solution sections of proposals, and present to client architecture review boards



Mentor 5 10 engineers; set the technical bar in design reviews and code reviews



Must have



GCP, deep: BigQuery, GKE, Cloud Run, Cloud Storage, VPC design and VPC Service Controls, IAM and org policies, Cloud Composer, Pub/Sub, Cloud Build, Artifact Registry, Cloud Logging/Monitoring



Azure, working depth: landing zones per Cloud Adoption Framework, AKS, Azure Storage/ADLS Gen2, Azure Data Factory, Entra ID, Azure Policy, Azure



Monitor, Azure DevOps



Infrastructure: networking that you can whiteboard from memory routing, peering, private endpoints, hybrid connectivity, firewall and egress design



SQL at an expert level, plus Python for tooling and automation



Terraform in production, with modules you have authored and maintained



Data modelling dimensional and denormalised, and the judgement to know when each is right



Demonstrated ownership of a production platform at meaningful scale: TB-to-PB warehouse, 100+ source systems, or 500+ BI users



Experience running a multi-cloud or hybrid estate in reality, not just in a diagram



Client-facing communication strong enough to hold a room of skeptical senior stakeholders



Strongly Preferred



Looker / LookML hands-on: development and administration. This is the single biggest differentiator among otherwise equal candidates



Dataform or dbt at scale, with a real promotion process between environments



FinOps track record a specific cost reduction you can quantify and explain



Migration experience: on-prem to cloud, or BI tool consolidation (Domo, Tableau, Power BI, Qlik Looker)



Exposure to production GenAI or agentic workloads on cloud infrastructure



Certifications: GCP Professional Cloud Architect or Professional Data Engineer; Azure Solutions Architect Expert (AZ-305); Looker LookML Developer



Nice to have



Kafka or Confluent; streaming-first architectures



Databricks, Snowflake, or Fabric comparative experience



Regulated-industry background (BFSI, healthcare, telco) with the compliance posture that comes with it



Pre-sales or partner co-sell experience with a hyperscaler field team