What Success Looks Like (Performance Objectives)
1)
Multi-Cloud Ingestion (E.g. AWS / Azure / GCP)
Within 180 days, we will deliver ingestion services that process high-volume billing data with clear SLAs.
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AWS ingestion supports CUR files in S3, Cost Explorer APIs, Cost Categories, RI/Savings Plans coverage.
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Azure ingestion supports Cost Management exports/APIs, EA/billing constructs where applicable.
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GCP ingestion supports BigQuery billing export tables and relevant cost APIs.
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Ingestion can handle TB-scale datasets with partitioning/compaction strategies and repeatable backfills.
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Data quality checks and lineage are built-in (schema drift detection, late-arriving data handling, idempotency).
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≥ 99% ingestion job success rate (or agreed SLA)
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Known error budget and automated retries/alerts
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Backfills complete within agreed time windows
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Cost data freshness targets are met (e.g., daily/hourly depending on provider constraints)
2)
Unified Cost Data Model & Normalization
Within 6-9 months, we continue the story with the ability to deliver a normalized, cross-cloud cost model enabling allocation and analytics consistency.
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A unified schema that resolves cross-cloud differences (accounts/subscriptions/projects, resource identity, usage types).
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Support for allocation via tags/labels, custom dimensions, shared cost modeling, and business mappings.
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A detailed semantic layer (definitions for amortized vs. blended, commitment allocation, etc.).
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A versioned approach for schema evolution and backward compatibility.
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Allocation accuracy validated with FinOps customers
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Clear “source of truth” definitions and reconciliation process
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Schema changes do not break downstream dashboards/queries
3)
High-Performance Analytics Engine (Interactive + Explainable)
Within 9–12 months, we will enable fast queries for “where did the spend go?”
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Analytics pipelines optimized for interactive exploration (e.g., drill-downs, group-by dimensions, cost drivers).
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Support for anomaly detection workflows (rules-based and/or ML-assisted is a plus).
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Query patterns built for performance at scale (partitioning, clustering, materialization strategy).
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Integration with warehouse/Lakehouse depending on your stack.
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Key dashboards load within target latency (e.g., <2–5 seconds for common views)
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Defined SLAs for compute cost, query performance, and data freshness
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Reduction in support critical issues tied to data discrepancies
4)
Product-Grade Full Stack Delivery (Backend + UI)
Within 90–180 days, end-user capabilities will be shipped to accelerate FinOps workflows.
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Backend services in Python, expose stable APIs for analytics and allocations.
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TypeScript UI (React/Next.js) supports high-performance tables, explorers, and filters.
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Reusable API patterns and consistent contract/versioning.
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Thoughtful UX for FinOps use cases: allocation review, cost driver exploration, commitment coverage insights.
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Feature adoption and repeat usage in pilot teams
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Reduction in manual reporting effort for FinOps teams
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Performance benchmarks meet agreed targets
5)
Engineering Standards, Reliability & Cost Efficiency!
Ongoing, we will raise the platform bar across security, observability, and operational perfection.
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Standards for microservices, API architecture, data modeling, distributed system patterns.
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Observability: tracing/logging/metrics, SLOs, alerting, on-call readiness where applicable.
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Security and governance appropriate for billing and usage data (RBAC, audit, encryption, least privilege access).
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Cost-aware engineering: FinOps principles applied to the platform itself.
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Production incidents reduced over time, with blameless postmortems and preventive action
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Infrastructure costs supervised and optimized with clear ownership
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Engineering guidelines accepted by the team and reflected in PR quality
Your Core Responsibilities (What You’ll Do)
a) Architect and build ingestion, normalization, and analytics components for multi-cloud billing data.
b) Lead reviews and establish engineering practices for reliability, scalability, and cost efficiency.
c) Deliver high-quality backend services and modern UI experiences for FinOps users.
d) Partner with FinOps Reporting and Analytics, Product, and Cloud Engineering to build roadmap and ensure correctness.
e) Mentor engineers through code reviews, pairing, design guidance, and technical standards.