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