# Senior Software Engineer - Multi-Cloud FinOps Platform **Company**: London Stock Exchange Group (LSEG) **Location**: London **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Finance **Apply**: https://lseg.wd3.myworkdayjobs.com/en-US/Careers/job/Bucharest-Romania/Senior-Full-Stack-FinOps-Engineer_R0119143-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_229d08c4-f9d ## Description We're looking for a Senior Software Engineer to join our team, focusing on a next-generation multi-cloud FinOps platform. You'll own architecture and critical components end-to-end, raising engineering standards and partnering with FinOps practitioners and product leadership to ship measurable impact. **What Success Looks Like (Performance Objectives)** - 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) - 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 - 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., <2–5 seconds for common views) - Defined SLAs for compute cost, query performance, and data freshness - Reduction in support critical issues tied to data discrepancies - 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 - 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)** - Architect and build ingestion, normalization, and analytics components for multi-cloud billing data. - Lead reviews and establish engineering practices for reliability, scalability, and cost efficiency. - Deliver high-quality backend services and modern UI experiences for FinOps users. - Partner with FinOps Reporting and Analytics, Product, and Cloud Engineering to build roadmap and ensure correctness. - Mentor engineers through code reviews, pairing, design guidance, and technical standards. **The Environment (Representative Tech Stack)** - 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: - Develop production ingestion pipelines for large datasets (TB-scale or high-frequency) with quality controls and SLAs. - Crafted multi-tenant or multi-account analytics platforms with consistent schemas and performance constraints. - Implemented cost/usage analytics or similar domains (telemetry, observability, billing, metering, FinOps, data platforms). - Shipped full stack features: APIs + UI that users rely on daily (not just prototypes). - Led architecture decisions and influenced standards across a team (architecture design documents, reviews, migration strategy). - Worked with CUR/exports, commitment models, allocation, tagging/labels, reconciliation. **Critical Proficiencies (What “Extraordinary” Looks Like in the Role)** - Systems thinking: sees end-to-end flow and optimizes for reliability + cost + usability. - Data correctness perspective: makes data credible, explainable, and defensible. - Product engineering: builds for users, not just infrastructure. - Pragmatic architecture: chooses simple, scalable approaches; avoids over-engineering. - Influence & mentorship: improves via coaching, standards, and streamlined communication. ## Skills ### Required - Python - TypeScript - FastAPI - Flask - Django - Spark - PySpark - Dask - Snowflake - BigQuery - Redshift - Synapse - Docker - Kubernetes - Terraform - CDK - Pulumi - OpenTelemetry --- Source: [Apply at lseg.wd3.myworkdayjobs.com](https://lseg.wd3.myworkdayjobs.com/en-US/Careers/job/Bucharest-Romania/Senior-Full-Stack-FinOps-Engineer_R0119143-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)