Senior Backend Developer

Description Power the Future with us! At SolarEdge (NASDAQ: SEDG), we're a global leader in smart energy technology, with over 3,000 employees, offices in 34 countries, and millions of installations worldwide. Our innovative solutions include solar inverters, battery storage, backup systems, EV charging, and AI-based energy management. We're committed to making clean, green energy the primary power source for homes, businesses, and beyond. With the growing demand for electricity, the need for smart, clean energy sources is constantly rising. SolarEdge offers amazing opportunities to develop your skills in a multidisciplinary environment, covering everything from research and development to production and customer supply. Work with talented colleagues, tackle exciting challenges, and help create a sustainable future in an industry that's always evolving and innovating. Join us and be part of a company that values creativity, agility, and impactful work. SolarEdge is looking for a talented Senior Backend Developer to join our Alerts and Insights Platform team. someone who could deep dive into data to understand what the numbers are actually saying and writing production-grade services. In this role you will design and build the backend systems that power our analytics and alerting platform, while also owning the algorithm and data exploration work that feeds them. You will translate raw photovoltaic (PV) monitoring data into meaningful insights and actionable alerts that help installers, system owners, and internal teams maximize energy yield and detect faults early. This is not a pure engineering role and not a pure data role — it sits deliberately at the intersection. The ideal candidate writes clean, scalable backend code AND gets excited about querying large datasets to understand an anomaly. Research curiosity and engineering discipline, in equal measure. What You'll Do Design and build scalable backend services and APIs that power PV monitoring, alerting, and analytics features — from data ingestion through to end-user-facing outputs. Own algorithm development end-to-end: from exploratory data analysis and prototyping to production-hardened implementations deployed at scale across millions of monitored devices. Conduct data exploration and research to understand system behavior, surface patterns, and generate hypotheses — using SQL, Python, and visualization to iterate quickly before formalizing. Implement and optimize anomaly detection, performance degradation analysis, fault classification, and forecasting algorithms on real-world PV time series data. Build and maintain data pipelines that transform raw telemetry into features, alerts, and insights — collaborating with data engineers to make algorithmic requirements scale-ready. Define and track quality KPIs (precision, recall, latency, computational cost) and iterate continuously to improve algorithm performance and business impact. Leverage ML and statistical techniques (time series modeling, supervised/unsupervised learning, statistical inference) where they add real value — without over-engineering. Write and optimize complex SQL for data exploration, feature engineering, validation, and ad-hoc investigation across large distributed datasets. Document design decisions, trade-off analyses, and implementation workflows; actively contribute to code reviews and team knowledge sharing. Stay current with advances in backend architecture, data engineering, and ML; propose and evaluate new methods and tools where they add value. Requirements BSc / MSc in Computer Science, Software Engineering, Data Science, Electrical Engineering, or a closely related field. 5+ years of backend development experience — designing and shipping production services, APIs, and data-intensive systems in Python (primary) and ideally one additional language. Strong software engineering fundamentals: system design, RESTful / event-driven architectures, clean code, testing, and an eye for maintainability. Genuine data curiosity: comfortable and enthusiastic about digging into large datasets, forming hypotheses, and iterating through exploratory analysis to extract insight. Professional-level SQL: fluent with complex queries, window functions, aggregations, and data validation at scale. Familiarity with ML and data science: you don’t need to be a research scientist, but you should understand and be able to apply supervised/unsupervised learning, time series analysis, and statistical modeling in code. Experience with data engineering workflows: ETL/ELT patterns, feature pipelines, data quality — and the ability to collaborate with data engineers to translate algorithm requirements into scalable infrastructure. Understanding of algorithm trade-offs: accuracy vs. latency, model complexity vs. maintainability, precision vs. recall — and the ability to make and communicate informed decisions. Entrepreneurial mindset: takes full ownership end-to-end, works independently, raises problems early, and brings new ideas proactively. Clear communicator: able to explain complex technical decisions and analytical findings to non-technical stakeholders (product, support, operations). Advantages: Domain knowledge in PV / energy systems: hands-on experience with inverter data, string-level monitoring, irradiance modeling, or power electronics. Time series expertise: seasonality decomposition, change-point detection, forecasting models. Experience with large-scale data platforms: Spark, Databricks, BigQuery, Snowflake, Redshift, or similar. Generative AI / LLM workflows: fine-tuning, RAG, prompt engineering, evaluation, orchestration (LangChain, LangGraph). MSc or higher in Computer Science, Data Science, Physics or a related quantitative field.