Role Scope Single architect covering both: (1) Salesforce-to-GCP data pipeline build, (2) QUIVER tool deployment This is one combined hire, not being split across two roles Location Must be Bay Area-based Onsite availability required — client is a California government entity Standard public-sector vetting/background check should be expected Employment Type G5 or G6 level preferred (budget-capped) Open to full-time employees and contractors Technical Capability Deep GCP data platform expertise: pipeline construction, middleware architecture, job scheduling Strong, production-level BigQuery skills: data modeling, performance tuning, partitioning/clustering, slot management, cost optimization Hands-on with GCP data stack: Dataflow (Apache Beam), Pub/Sub, Cloud Composer/Airflow, Cloud Storage, Dataform or dbt,
Dataplex, Cloud Data Fusion Salesforce:
functional/admin SFDC knowledge NOT required, but must understand technical constraints — API limits, connector modes (Bulk API, CDC/Platform Events) — and be able to articulate how to design around them
QUIVER:
must be able to work with job definition monitoring, configure autofix capabilities, and validate agent-driven defect fixes Strong SQL and Python; Terraform (IaC); CI/CD for data pipelines GCP security fundamentals:
IAM, VPC
Service Controls, encryption, PII handling Able to explain end-to-end connectivity architecture (SFDC → GCP) clearly and in own words — this is the key screening test to verify hands-on experience FULL JD Google Data Platform Architect —
10+ years in data engineering/platform architecture, with 4+ years hands-on GCP experience
Location:
Onsite Role Summary We are looking for a Google Data Platform Architect to own the technical design and delivery of a Salesforce-to-GCP data pipeline program, and to lead the deployment of QUIVER, Tenarai's intelligent DataOps accelerator, on top of the GCP data platform. This is a dual-mandate role. The first mandate is to land Salesforce CRM data into BigQuery reliably, securely, and at a predictable cost. The second is to make that platform observable, governed, self-healing, and cost-transparent by implementing QUIVER against the GCP stack — so the client inherits an operating model, not just a set of pipelines. This role is expected to be hands-on. T hey will write code, run design reviews with client architects, and be accountable for the platform working in production. Key Responsibilities Workstream 1 — Salesforce to GCP Data Pipeline Own the end-to-end target architecture: Salesforce source objects → landing → curated → consumption layers in BigQuery, following a medallion (bronze/silver/gold) pattern. Select and justify the ingestion approach across the available options — Salesforce Bulk API 2.0, Change Data Capture and Platform Events, Cloud Data Fusion Salesforce connector, Datastream, Salesforce Data Cloud zero-copy sharing, or a third-party CDC tool — and document the trade-offs on latency, cost, API limit consumption, and operational burden. Design incremental and full-refresh strategies for standard and custom SFDC objects, including handling of formula fields, deleted/archived records, soft deletes, and Salesforce governor and API limits. Build streaming paths using Pub/Sub and Dataflow where near-real-time is justified; batch paths using Cloud Storage staging and BigQuery load jobs where it is not. Orchestrate with Cloud Composer (Airflow 2.x); implement transformation logic in Dataform or dbt with version control, CI/CD, and environment promotion. Model the curated layer for downstream consumption in Looker and the client's reporting estate; define partitioning, clustering, and materialization strategy for query performance and cost.
Implement security and compliance controls:
IAM role design, VPC Service Controls, CMEK, Secret Manager, and Cloud DLP for PII in CRM data. Own BigQuery cost architecture — editions and slot reservations vs. on-demand, storage tiering, and workload isolation. Lead data reconciliation between Salesforce and BigQuery, and define the acceptance criteria the client signs off against. Workstream 2 — QUIVER Implementation Deploy QUIVER as the centralized operations cockpit across the GCP estate, without replacing or duplicating tooling the client already owns. Instrument the four QUIVER pillars against GCP-native telemetry.
Implement predictive alerting:
consumption trend warnings and pattern-based failure prediction from historical log patterns and runtime overflow — prevention, not just post-failure retry. Integrate QUIVER alerting and remediation workflows into the client's ITSM and change management process. Define and hand over the run book, support model, and operating cadence. Cross-cutting Run architecture review board sessions and secure client-side security, privacy, and architecture approvals. Produce the artifacts the engagement is judged on: target-state architecture, source-to-target mappings, data contracts, non-functional requirements, and cutover/runbook documentation. Mentor engineers on the delivery pod and enforce engineering standards through code review. Support pre-sales and account growth with solution shaping and estimation when asked. Required Skills and Experience 10+ years in data engineering or data platform architecture, including 4+ years designing and delivering on Google Cloud. Deep, production-level BigQuery expertise: data modeling, performance tuning, partitioning and clustering, slot and reservation management, cost optimization. Hands-on with the GCP data stack — Dataflow (Apache Beam), Pub/Sub, Cloud Composer/Airflow, Cloud Storage, Dataform or dbt, Dataplex, Cloud Data Fusion. Demonstrated experience integrating Salesforce as a data source at enterprise scale, including working knowledge of the Salesforce data model, Bulk API, CDC/Platform Events, and API governor limits. Strong SQL and Python; infrastructure-as-code with Terraform; CI/CD for data pipelines. Practical experience with data quality, observability, and lineage tooling, and with designing operational alerting that engineers actually act on. Cloud security fundamentals on
GCP:
IAM, VPC
Service Controls, encryption, PII handling. Proven ability to run technical conversations with enterprise client architects and security teams, and to defend design decisions. Preferred Google Cloud Professional Data Engineer or Professional Cloud Architect certification. Experience implementing a DataOps, observability, or FinOps platform (e.g. Monte Carlo, Collibra, Ataccama, Atlan, Unravel) Prior multi-cloud exposure (Azure Data Factory, Databricks, AWS) for hybrid estates. Looker or LookML modeling experience. Consulting or systems integrator background with client-facing delivery accountability.
