Shape the future of data by designing, developing, and maintaining robust data pipelines and platforms. This role empowers analytics, reporting, and AI/ML initiatives. You'll tackle moderately complex data engineering challenges independently, collaborating closely with business and technology teams to deliver data solutions that meet enterprise needs. As a technical resource, you'll guide team members on best practices and drive continuous improvement in our data platforms and engineering processes.

Key responsibilities include designing and building scalable data ingestion, transformation, and integration pipelines. You will develop and optimize ETL/ELT workflows, analyze business requirements to create technical data solutions, and resolve complex engineering problems. Maintaining data models, quality frameworks, and validation processes is crucial, as is collaborating with cross-functional teams to deliver trusted data assets. Performance optimization, cloud platform support, and implementing data governance, security, and compliance standards are also core duties. You'll also create documentation, participate in code reviews, and troubleshoot production issues to ensure sustainable solutions and contribute to automation and CI/CD initiatives.

A Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related engineering field is required, along with over 3 years of experience in data engineering, software engineering, or big data development. Proficiency in developing ETL/ELT pipelines, working with large datasets, and modern data architectures such as data warehouses and data lakes is essential. Strong skills in SQL and Python for data processing, along with knowledge of cloud platforms like Azure, AWS, or Google Cloud, are necessary. An understanding of data modeling, quality, and governance principles, coupled with proven analytical and problem-solving abilities, is vital. Excellent communication skills and the capacity to work independently are also expected.