Software Development Engineer, Market Operations & Structuring

As a Software Development Engineer on the Market Operations & Structuring team at Arevon, your responsibilities will include building stable Extraction, Transformation, and Loading (ETL) data pipelines, designing backend systems for managing power market data, and automating processes to enhance data delivery. You'll collaborate closely with data scientists to deploy predictive models and maintain performance report dashboards, ensuring a comprehensive approach to Arevon's energy storage portfolio.

To qualify for the Software Development Engineer position at Arevon, candidates should have at least 2 years of experience in software engineering roles, along with strong programming skills in languages like Python or Java. Additional qualifications include experience in building data pipelines, working with big data tools, and familiarity with cloud platforms such as AWS or Azure. A Master’s degree in Applied Math, Statistics, or Computer Science will significantly enhance your credentials.

For the Software Development Engineer role at Arevon, proficiency in at least one scripting or programming language such as Python, Java, or C++ is essential. These skills will be critical as you will be developing software solutions and managing complex data structures to support Arevon’s innovative energy storage projects.

Prior work experience in the energy, renewable energy, or energy storage industries is highly beneficial for aspiring candidates of the Software Development Engineer position at Arevon. Additionally, familiarity with big data technologies, API development, and cloud computing platforms will enhance your ability to seamlessly integrate and manage large datasets essential for powering our projects.

Experience with data pipelines is crucial for the Software Development Engineer role at Arevon because you'll be tasked with building and maintaining ETL processes that convert large datasets into actionable insights. Your ability to handle big data technologies and optimize data pipeline performance will be key factors during the selection process.

To build an effective ETL pipeline for energy data, I would first identify the data sources and the relevant metrics to extract. Then, I would select appropriate tools and scripts to transform the data into a suitable format for analysis, ensuring to handle any data quality issues. Finally, I would load the transformed data into a storage solution while maintaining documentation and monitoring for ongoing adjustments.

Absolutely! I have hands-on experience with various cloud computing services, particularly AWS. I’ve worked on deploying applications, managing databases, and leveraging big data services like AWS Glue and Redshift. My focus has been on optimizing performance and ensuring secure data management practices.

I am most proficient in Python and Java, as these languages have been my primary tools for software development. With Python, I usually develop data analysis scripts and machine learning models, while Java has been my go-to for building scalable applications, particularly in cloud environments.

To ensure scalability in my software solutions, I focus on modular design principles that allow for components to be independently updated and scaled. I also leverage cloud services to dynamically allocate resources based on demand and monitor application performance to identify bottlenecks that may require optimization.

I use several strategies including efficient data caching mechanisms, asynchronous processing, and parallelized execution of tasks to optimize data delivery pipelines. Additionally, I continually analyze pipeline performance metrics to identify and mitigate potential bottlenecks before they impact system efficiency.

ETL processes involve three key stages: Extraction of data from multiple sources, Transformation of that data into a manageable format, and Loading the processed data into a data warehouse or analytical environment. My understanding includes ensuring data integrity and consistency throughout these phases to support analytical decision-making.

In my last role, I developed a software solution to automate data reporting processes that were previously manual and time-consuming. By analyzing the existing workflow, I was able to identify pain points, design a streamlined process using APIs for data sourcing, and implement dashboards that provided real-time insights—significantly enhancing productivity.

I stay current with software development trends by regularly reading industry blogs, participating in online forums, and attending tech meetups and webinars. I also take online courses to enhance my programming skills and stay informed about new tools and frameworks to apply in my projects.

I have developed several APIs using frameworks like Flask and Django, focusing on creating RESTful services that allow seamless communication between different software systems. My experience includes designing endpoints, ensuring security through authentication, and maintaining detailed API documentation for users.

To address unexpected data anomalies in production, I would implement monitoring systems that alert the team to inconsistencies as they arise. After receiving an alert, I'd conduct a systematic investigation to identify the source of the anomaly and the extent of its impact. From there, I would apply corrective measures, update documentation, and improve our data validation processes to prevent similar occurrences in the future.