Software Engineer I- Eng

Job Description

Assist in building and maintaining data pipelines that collect, transform, and process data. Support the development and testing of ETL/ELT workflows. Monitor data pipelines and help troubleshoot production issues. Work with engineers to ensure data quality, accuracy, and reliability. Write and optimize SQL queries for data analysis and reporting. Contribute to the development of data models and analytics solutions. Participate in code reviews and learn software engineering best practices. Help automate manual operational tasks and improve engineering efficiency. Create and maintain technical documentation for data processes and workflows. Collaborate with product managers, software engineers, and data engineers in an Agile environment. Learn and apply cloud technologies, DevOps practices, and modern data engineering tools. Bachelor's degree in Computer Science, Information Technology, Information Systems, Statistics, Mathematics, or a related technical field. Strong understanding of programming fundamentals and data structures. Knowledge of Python, Java, or another programming language. Basic understanding of SQL and relational databases. Familiarity with object-oriented programming concepts. Understanding of software development lifecycle and version control systems such as Git. Eagerness to learn new technologies and work in a collaborative team environment. Internship, academic project, or coursework involving data engineering, analytics, or cloud technologies. Exposure to cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure. Familiarity with data warehousing concepts, ETL processes, or big data technologies. Understanding of Agile development methodologies such as Scrum or Kanban. Experience with Python data libraries, SQL projects, or analytics dashboards. What You'll Learn Cloud-native data engineering on Google Cloud Platform (GCP) Data pipelines, ETL/ELT, and large-scale data processing Data modeling and analytics platforms CI/CD and DevOps practices Observability, monitoring, and operational excellence AI-ready data platforms and modern data architectures