Senior Full Stack FinOps Engineer

We engineer the core services and experiences of a next-generation multi-cloud FinOps platform -turning TB-scale billing/usage data into trusted, fast, explainable cost intelligence that enables allocation, optimization, and operational accountability.

You’ll own architecture and critical components end-to-end (ingestion → normalization → analytics → APIs → UI), raise engineering standards, and partner closely with FinOps practitioners and product leadership to ship measurable impact.

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.

Key outcomes

• AWS ingestion supports CUR files in S3, Cost Explorer APIs, Cost Categories, RI/Savings Plans coverage.

• Azure ingestion supports Cost Management exports/APIs, EA/billing constructs where applicable.

• GCP ingestion supports BigQuery billing export tables and relevant cost APIs.

• Ingestion can handle TB-scale datasets with partitioning/compaction strategies and repeatable backfills.

• Data quality checks and lineage are built-in (schema drift detection, late-arriving data handling, idempotency).

Success measures

• ≥ 99% ingestion job success rate (or agreed SLA)

• Known error budget and automated retries/alerts

• Backfills complete within agreed time windows

• 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.

Key outcomes

• A unified schema that resolves cross-cloud differences (accounts/subscriptions/projects, resource identity, usage types).

• Support for allocation via tags/labels, custom dimensions, shared cost modeling, and business mappings.

• A detailed semantic layer (definitions for amortized vs. blended, commitment allocation, etc.).

• A versioned approach for schema evolution and backward compatibility.

Success measures

• Allocation accuracy validated with FinOps customers

• Clear “source of truth” definitions and reconciliation process

• 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?”

Key outcomes

• Analytics pipelines optimized for interactive exploration (e.g., drill-downs, group-by dimensions, cost drivers).

• Support for anomaly detection workflows (rules-based and/or ML-assisted is a plus).

• Query patterns built for performance at scale (partitioning, clustering, materialization strategy).

• Integration with warehouse/Lakehouse depending on your stack.

Success measures

• Key dashboards load within target latency (e.g., <2>

• Defined SLAs for compute cost, query performance, and data freshness

• 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.

Key outcomes

• Backend services in Python, expose stable APIs for analytics and allocations.

• TypeScript UI (React/Next.js) supports high-performance tables, explorers, and filters.

• Reusable API patterns and consistent contract/versioning.

• Thoughtful UX for FinOps use cases: allocation review, cost driver exploration, commitment coverage insights.

Success measures

• Feature adoption and repeat usage in pilot teams

• Reduction in manual reporting effort for FinOps teams

• Performance benchmarks meet agreed targets

5) Engineering Standards, Reliability & Cost Efficiency!

Ongoing, we will raise the platform bar across security, observability, and operational perfection.

Key outcomes

• Standards for microservices, API architecture, data modeling, distributed system patterns.

• Observability: tracing/logging/metrics, SLOs, alerting, on-call readiness where applicable.

• Security and governance appropriate for billing and usage data (RBAC, audit, encryption, least privilege access).

• Cost-aware engineering: FinOps principles applied to the platform itself.

Success measures

• Production incidents reduced over time, with blameless postmortems and preventive action

• Infrastructure costs supervised and optimized with clear ownership

• 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.

The Environment (Representative Tech Stack)

(We care more about your ability to engineer outcomes than exact tool matches.)

• Languages: Python, TypeScript
• Backend: FastAPI / Flask / Django; microservices; event-driven patterns.
• Data/Compute: Spark/PySpark/Dask; serverless ETL (Glue/Synapse/Dataflow)
• Storage/Warehouse: Snowflake, BigQuery, Redshift, Synapse; Lakehouse patterns
• Infra: Docker/Kubernetes; IaC (Terraform/CDK/Pulumi)
• Observability: OpenTelemetry-style tracing, metrics/logs, SLOs
• Clouds: Cloud billing constructs and APIs

Candidate Profile (Evidence of Comparable Accomplishments)

You’re likely a strong fit if you’ve done several of these:

a) Develop production ingestion pipelines for large datasets (TB-scale or high-frequency) with quality controls and SLAs.

b) Crafted multi-tenant or multi-account analytics platforms with consistent schemas and performance constraints.

c) Implemented cost/usage analytics or similar domains (telemetry, observability, billing, metering, FinOps, data platforms).

d) Shipped full stack features: APIs + UI that users rely on daily (not just prototypes).

e) Led architecture decisions and influenced standards across a team (architecture design documents, reviews, migration strategy).

f) Worked with CUR/exports, commitment models, allocation, tagging/labels, reconciliation.

Critical Proficiencies (What “Extraordinary” Looks Like in the Role)

1. Systems thinking: sees end-to-end flow and optimizes for reliability + cost + usability.

2. Data correctness perspective: makes data credible, explainable, and defensible.

3. Product engineering: builds for users, not just infrastructure.

4. Pragmatic architecture: chooses simple, scalable approaches; avoids over-engineering.

5. Influence & mentorship: improves via coaching, standards, and streamlined communication.

Career Stage:

London Stock Exchange Group (LSEG) Information:

Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.

LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.

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