Partnerships and Deal Evaluation Questions

Approach: I’d treat onboarding as a gated technical acceptance process with measurable tests and sign-off steps. Below is a compact due-diligence checklist (what I require) and a red-flag list (stop/mitigate before integration).

Due-diligence checklist

• Requirements & contract SLAs
  • Agreed throughput SLAs (requests/sec), p95 latency targets, error-rate thresholds, retry/backoff rules, maintenance windows, uptime (e.g., 99.95%), liability and data breach clauses, RTO/RPO.
• API readiness & compatibility
  • OpenAPI/Swagger spec, versioning policy, auth (OAuth2/JWT or mTLS), idempotency keys, example payloads, pagination, webhook semantics and signing.
  • Sandbox endpoint with representative data and deterministic test harness.
• Throughput & performance
  • Capacity planning: expected peak TPS, burst capacity, concurrency limits.
  • Load-test report: synthetic tests at 2x expected peak, latency percentiles, resource usage.
  • Rate limiting behavior and graceful degradation strategy.
• Security posture
  • SOC2 / ISO27001 / PCI scope evidence, vulnerability scan / pen-test reports (last 12 months), data encryption at-rest/in-transit, key management, secrets rotation policy, least-privilege IAM.
  • Incident response plan, contact/escilation matrix, responsible disclosure process.
• Data quality & contracts
  • Schema definitions, field-level SLAs (accuracy, freshness), sample data, uniqueness constraints, timezone/currency handling, expected error codes and validation rules.
  • Data retention & GDPR/CCPA compliance, PII minimization.
• Operational capacity & support
  • On-call roster, MTTR targets for critical incidents, runbooks for common failures, monitoring dashboards, alerting thresholds and test alerts.
  • Integration cadence: points of contact, weekly onboarding checkpoints, rollback plan.
• Error handling & observability
  • Standardized error codes, retry semantics, dead-letter handling for webhooks/events.
  • Traceability: request IDs, structured logs, distributed tracing headers, metrics (success rate, median/p95 latency, queue depth).
• Test plan & acceptance
  • End-to-end test suite, chaos tests for transient failures, webhook delivery verification, reconciliation tests (orders, refunds).
  • Pilot period metrics review and final cutover checklist.

Red flags (blockers or require mitigation plans)

• No sandbox or only limited non-representative test data.
• No API spec, undocumented endpoints or inconsistent versioning.
• Missing authentication standards, plaintext tokens, or inability to support secure auth flows (OAuth2/mTLS).
• Lack of performance/load-test evidence or inability to support projected peak TPS.
• No SLA or refusal to commit to error-rate/latency/uptime guarantees.
• No recent security assessments, no formal incident response, or inability to provide compliance evidence for PCI/SOC2 where required.
• Poor data quality examples: missing required fields, inconsistent IDs, timezone/currency ambiguity.
• No monitoring, no on-call rota, or excessive MTTR in past incidents.
• Opaque billing/contract terms, unbounded rate limiting that drops traffic without clear error semantics.
• Refusal to implement idempotency or guaranteed-at-least-once/deduplication for critical flows.

Recommended acceptance gating

1. Contract signed with SLAs and security clauses.
2. Successful sandbox integration + automated E2E tests.
3. Load-test passed at 2x expected peak with acceptable p95 latency and <0.1% error for critical flows.
4. Security sign-off (pen test remediation or compensating controls).
5. Pilot (limited market/region) with reconciliation and agreed rollback triggers.
6. Production cutover after operational runbook and monitoring are validated.

This ensures technical feasibility, predictable operations, and clear contractual protections before full integration.

Situation: A retail client needed a scalable, compliant managed database for their new omnichannel order service. Shortlist: AWS RDS (Aurora) vs. Azure Cosmos DB. They required sub-100ms read latency, 99.99% availability, PCI-DSS readiness, predictable costs, and ability to geo-replicate for regional failover.

Task: I led vendor evaluation and ran PoCs to recommend the best fit balancing performance, compliance, TCO, and integration with their cloud roadmap (they planned multi-year Azure migration but had heavy AWS usage today).

Action:

• Defined evaluation criteria with stakeholders:
  • Functional: ACID transactions, read/write patterns, global replication
  • Non-functional: latency, availability, backup/RTO, scalability
  • Security/Compliance: PCI, encryption at rest/in transit, audit logs
  • Operational: monitoring, runbook maturity, managed backups, SLA
  • Commercial: licensing, predictable TCO, vendor lock-in risk
• Designed two 2-week PoCs mirroring production load (synthetic traffic matching 10k RPS peaks, mixed reads/writes, failover scenarios).
  • Measured p99 latency, failover recovery time, replication lag, backup/restore time.
  • Validated compliance controls via configuration review and sample audit evidence.
  • Assessed developer experience: migration effort, SDKs, operator runbooks.
• Scored each vendor against criteria and ran cost models for 3-year TCO including data egress and scaling scenarios.
• Presented results to client with risk matrix and migration roadmap options (lift-and-shift vs. gradual Azure consolidation).

Result: Aurora delivered lower p99 latency (70ms vs Cosmos 140ms for transactional workloads), simpler ACID semantics for their existing schema, and mature point-in-time recovery; Cosmos excelled in global distribution but would require significant app changes and higher operational cost. Recommendation: choose Aurora for initial launch with a documented future-proof abstraction layer (data access service) to allow phased move to Cosmos if full Azure consolidation occurs. Client accepted recommendation, launched on schedule, met SLAs, and lowered projected 3-year cost by ~18% versus Cosmos alternative.

Learning: Align technical benchmarks to real business constraints (compliance, cloud roadmap) and design for change (abstraction) to minimize lock-in while optimizing near-term delivery.