Sr Software Engineer
Listed on 2026-08-30
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Software Development
DevOps, Cloud Engineer - Software, AI Engineer (Applied/Software)
RWE Americas, LLC To start as soon as possible, full time, permanent
Functional area:
IT / Digital Remuneration:
Exempt
Join RWE America’s Digital Solutions team and lead the critical software architecture driving our grid-scale renewable energy operations technology stack. In this role, you will take end-to-end engineering ownership of a high-throughput, custom data ecosystem connecting live operational assets directly to leadership metrics across 160+ wind, solar, and battery storage sites.
The Sr Software Engineer I's core focus will be evolving an intelligent, predictive data platform that aggregates, normalizes, and categorizes performance downtime events. Acting as a central technical anchor, you will drive a clean, cloud-native microservices architecture, institutionalize high code standards across multiple languages, and provide active technical mentorship to a growing engineering squad.
Role Responsibilities :- Distributed Architecture:
Lead the end-to-end system design, robust implementation, and production scale of secure, event-driven microservices across Azure cloud environments - Intelligent Automation & AI:
Build and mature automated data processing flows, intentionally integrating Machine Learning (ML) anomaly detection models to systematically track, evaluate, and categorize industrial asset downtime - High-Throughput ETL Pipelines:
Architect and scale resilient background ingestion engines and low-latency APIs (REST, Web Sockets, AMQP protocols) capable of processing non-stop, continuous, high-volume operational telemetry data flows - Technical Anchor & Leadership:
Translate sophisticated business use cases directly into executable technical blueprints. Conduct comprehensive multi-language code reviews (C#/.NET, Python, Type Script, Go) to optimize security, code reuse, and overall system reliability - Proactive Observability:
Establish comprehensive monitoring, distributed logging, and real-time alerting systems (utilizing Prometheus, Grafana, and Azure Monitor) to build deep operational insight and radically minimize unplanned production downtime - Technical Documentation:
Architect comprehensive system design documents and technical blueprints, accelerating team velocity, streamlining developer onboarding, and ensuring operational readiness - Performance & Security:
Drive proactive security audits and performance tuning, establishing automated vulnerability remediation and code optimization frameworks across the platform - Incident Resolution:
Lead root-cause analysis for high-priority production incidents, collaborating across engineering teams to implement permanent preventative engineering fixes - CI/CD & Dev Ops:
Modernize Azure Dev Ops CI/CD pipelines to maximize deployment automation, accelerate release velocity, and improve developer efficiency
- Education Credentials:
Bachelor’s degree in Computer Science, Software Engineering, IT, or related fields—or equivalent proven track record of software engineering excellence in complex systems - Production
Experience:
5+ years of verified software engineering experience handling end-to-end production scale, service isolation, and lifecycle management within distributed, cloud-native enterprise environments - Multi-Language Polyglot Profile:
High production proficiency across C#/.NET, Python, and Type Script/React stacks, supplemented by a practical working familiarity with Go and robust relational/non-relational SQL database operations - Cloud Infrastructure & Dev Ops:
Hands-on engineering depth managing Azure cloud environments, production container orchestration (Kubernetes/AKS), Infrastructure-as-Code (Terraform/Helm charts), and declarative CI/CD release pipelines via Azure Dev Ops - Advanced Data Workflows:
Direct hands-on background constructing and managing automated high-volume ETL data jobs, utilizing workflow management schedulers like Apache Airflow. - AI/ML Framework Integration (Preferred):
Practical familiarity deploying, querying, or connecting to downstream data science models, statistical predictive analytics modules, or modern LLM APIs to drive automated multi-variable data classification - Complex Domain Exposure…
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