Data Engineer - Operating Data Automation

About the Position: As a Data Engineer , you will play a key role in supporting the and reporting of energy storage asset performance. Fluence is seeking engineers for a high[1]value project to automate the time-series operating data collected from utility-scale battery energy storage systems. The ideal candidate would: This is a great opportunity to grow your skills in cloud-based data infrastructure using AWS services while contributing to meaningful projects that help optimize the operation data management across a fleet of energy storage systems.

 1. Data Infrastructure & Transformation: · Design, maintain, and optimize data infrastructure for data collection, management, transformation, and access, focusing on scalability, reliability, and cost-effectiveness. · Continue to be hands-on with data integration engineering tasks, including data pipeline development, ELT processes, data integration and be the go-to expert for complex technical challenges. · Implement, and manage cloud infrastructure and automated workflows using AWS services (e.g., AWS - Step Functions, Batch,Glue, Athena,Lambda, EC2, Event bridge, ECS, Redshift), while optimizing existing orchestration solutions. · Monitor PostgreSQL performance and conduct troubleshooting to identify and resolve issues with database queries, performance bottlenecks, and availability. · Use Python and AWS cloud services to automate data retrieval and processing tasks.

2. Process Improvement and Efficiency · Identify opportunities for process improvement in data workflows, with a focus on automation and scalability. · Build and manage data warehouses, data lakes, and other data storage solutions to support large-scale data operations and analytics. · Document technical architectures, best practices, and operational procedures for orchestration workflows and automated infrastructure. · Demonstrate a willingness to develop problem-solving skills by participating in root cause analysis, gap analysis, and performance evaluations. · Exhibit strong time management skills and attention to detail, with the ability to manage multiple tasks and priorities in a dynamic environment. · Show eagerness to learn and apply new data analysis techniques, tools, and methodologies. · Ability to thrive in a fast-paced, evolving work environment while taking on new challenges.

3. Collaboration & Support: · Work closely with other team members to support ongoing data extraction and data pipeline needs. · Contribute to internal projects by documenting data workflows and helping with ad-hoc data pull requests.

 

 

• Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent work experience).

 • Experience: 5+ years of relevant experience as a data engineer or in a similar role, preferably with exposure to large-scale systems and energy-related data.

 • Technical Skills: o Programming: Python, Jupyter Notebooks o Databases: Strong experience with relational databases (MySQL, MariaDB, PostgreSQL) o Cloud Services: AWS S3, AWS Glue, AWS Batch, o Operating Systems: Linux (Proficiency with Linux-based environments) o Version Control & Collaboration: Agile methodologies, Atlassian Jira,GitHub

• Additional Skills: o Familiarity with Salesforce Asset Integration. o Proficiency in Microsoft Office (Excel, PowerPoint, etc.).