Enphase Energy is a global leader in solar inverters. By combining the power of solar energy and the proven advantages of communications technology, Enphase Energy makes solar power systems productive, reliable, smart and safe - increasing the energy harvest of solar panels by up to 25 percent. Our microinverter system is profoundly changing the way solar systems function, and thus, changing the solar industry itself.  As we continue our exciting growth, we are building teams with highly talented individual contributors and leaders who design, develop, and manufacture next generation solar technologies.  Our work environment is fast-paced, fun, and full of exciting new projects. The Software Engineer role will be responsible for Supporting Enphase Analytics and Data-warehouse team. Provides support for the data management/Governance activities of the organisation. Collaborates with Engineering/Quality/CS teams/Factory and various other teams. Overview: Design, develop, and maintain Analytics/dashboards/Automation and reports. Work closely with stakeholders to understand their needs and translate them into business/DW solutions. Ensure the performance optimisation, quality, and responsiveness of the dashboards. Collaborate with the team to define, design, and ship new features. Identify and correct bottlenecks and fix bugs. Help maintain code quality, organisation, and automatisation. Essential duties/responsibilities:Strong understanding of Datawarehouse Concepts. Experience translating theoretical concepts into engineered features, with a demonstrated ability to create features capturing important events or transitions within the data.Expertise in crafting custom features that highlight unique patterns specific to the dataset or problem, enhancing model predictive power. Ability to combine and synthesise information from multiple data sources to develop more informative features.Advanced knowledge in Apache Spark (PySpark, SparkSQL, SparkR) and distributed computing, demonstrated through efficient processing and analysis of large-scale datasets. Proficiency in Python, R, and SQL, with a proven track record of writing optimized and efficient Spark code for data processing and model training.Hands-on experience with cloud-based machine learning platforms such as AWS SageMaker and Databricks, showcasing scalable model development and deployment.Demonstrated capability to develop and implement custom statistical algorithms.Proficiency in statistical methods for identifying patterns and trends in large datasets, essential for predictive maintenance.Experience optimising code for performance in high-throughput, low-latency environments. Experience deploying models into production, with expertise in monitoring their performance and integrating them with CI/CD pipelines using AWS, Docker, or Kubernetes.Familiarity with end-to-end analytical architectures, including data lakes, data warehouses, and real-time processing systems.Experience creating insightful dashboards and reports using tools such as Power BI, Tableau, Incorta or custom visualisation frameworks to effectively communicate model results to stakeholders.5+ years of experience in data science and explored Gen AI/DL/AIML model using ML Flows Qualifications Bachelor’s or Master’s degree/ Diploma in Engineering, Statistics, Mathematics or Computer Science5+ years of experience as a Data Engineer/Data ScienceStrong problem-solving skillsProven ability to work independently and accuratelyStrong Expertise in Python, Spark, R, SQL or any other programming language Strong hands-on expertise with at least one analytics tools like Snowflake, Databricks or Incorta.