Join our dynamic team as a Data Engineer, contributing to innovative Engineering as a Service and Operate functions. You will be instrumental in building and maintaining robust data pipelines and platforms essential for driving analytics, reporting, and advanced AI/ML solutions.

This role involves tackling moderately complex data engineering challenges independently, collaborating closely with business and technology stakeholders to grasp requirements, and crafting reliable data solutions that meet enterprise-level needs. You will also serve as a key technical resource, offering guidance on best practices and actively participating in the continuous enhancement of our data platforms and engineering processes.

Design, develop, and maintain scalable data ingestion, transformation, and integration pipelines. Build and optimize ETL/ELT workflows for diverse data types from multiple sources. Translate business and customer requirements into effective technical data solutions. Address and resolve non-standard data challenges and complex engineering problems.

Develop and maintain sophisticated data models, data quality frameworks, and validation processes. Collaborate with data analysts, data scientists, product teams, and business stakeholders to deliver trusted data assets. Monitor and optimize pipeline performance, ensuring reliability, scalability, and cost-efficiency.

Support cloud-based data platforms and data warehouse/lakehouse environments. Implement comprehensive data governance, security, lineage, and compliance standards. Create detailed technical documentation, design specifications, and operational procedures. Participate in code reviews to uphold engineering best practices for development, testing, and deployment.

A Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field is required. We are looking for candidates with a minimum of 3 years of experience in data engineering, software engineering, or big data development.

Proven experience in developing ETL/ELT pipelines and working with large-scale datasets is essential. Familiarity with data warehouses, data lakes, and modern data architectures is expected. Strong proficiency in SQL and Python for data processing and engineering tasks is a must.

Knowledge of cloud-based data platforms such as Azure, AWS, or Google Cloud is necessary, along with an understanding of data modeling, data quality, and data governance principles. Demonstrated strong analytical and problem-solving skills, coupled with excellent communication and stakeholder collaboration abilities, are key. The ability to work independently with minimal supervision and manage multiple priorities effectively is also required.