Vendors must define and submit a minimum competency standard for all engineers deployed to the Data Platforms tower. Platform engineers must demonstrate genuine platform and infrastructure engineering capability across ten domains: Infrastructure as Code, Cloud Platform Management (AWS and Azure), CI/CD (including Harness), Databricks platform provisioning (not just pipeline development), Performance Tuning, Software Development (Python), Container Platform (Kubernetes), Networking, Security, and Disaster Recovery (build, maintain and support DR features and tooling). The competency standard must specify how each domain is assessed at onboarding and how sub-threshold engineers are identified, remediated, or removed.

Self-certification by the vendor is not acceptable and framework must include a CG-verifiable component.



The Caspian ingestion and curation framework (Databricks runtime, Airflow, Kafka, Dremio, Spark, Containers, Kubernetes, Python) must be maintained to meet CG’s published security SLAs, driven by vulnerability dashboards provided by CG’s security teams.

All upgrades must be validated in non-production environments and complete a platform burn-in process prior to promotion to production. Direct upgrades to production are not permitted.

Platform health dashboard available to CG showing cluster utilization, job success rates, and pending upgrades.

LASR Platform Maintenance — Azure

LASR ingestion and curation framework must be maintained — Azure Databricks runtime, Synapse integration, Dremio current, Python, Containers, Kubernetes, DR replication tooling.

LASR must remain patched, secure, and functional for its supported lifespan.



Version parity between LASR and Caspian is not applicable — architecturally distinct frameworks. The vendor’s obligation is to ensure LASR remains compatible with required cloud service interfaces and upstream/downstream system dependencies for as long as it is active, with no deferred patching or security debt.

Note: Security patching and vulnerability remediation are subject to the applicable SLAs defined in this RFP.

Infrastructure Upgrades — Airflow, Python, Databricks runtime

Core runtime dependencies (Python, Databricks runtime, Airflow) must be maintained within two major versions of current stable release. A documented, CG-approved exception is required for any deviation.

Vendors must propose an ongoing mechanism to monitor and manage upgrade and end-of-support risks in near-real time, with QBRs used to review trends and forward-looking risks.

Application Team Onboarding to Databricks

Vendor supports Client Group engineering teams on the Databricks platform through reactive onboarding support and platform feature development.

Self-Service Developer Utilities

Vendors must develop and enhance elf‐service tooling, automation, and documentation that enable Client Group engineering teams to implement, integrate, and operate routine platform features independently (including alignment with DevHub templates), with artifacts kept current as platform capabilities evolve.

Platform Health Monitoring Dashboards — real-time CG visibility

Real-time dashboards covering cluster utilization, job success/failure rates, storage costs, and pending alerts — directly accessible to CG without vendor involvement.

📌 Data/Platfrom Engineer (Guadalajara)
🏢 Pyramid Consulting
📍 Guadalajara