Join our dynamic team as a Senior Associate Data Engineer, playing a pivotal role in developing and maintaining robust data pipelines and platforms. This position is crucial for empowering analytics, reporting, and AI/ML initiatives across the enterprise.

As a key technical resource, you will independently tackle complex data engineering challenges. You will collaborate closely with business and technology stakeholders to define requirements and deliver reliable data solutions that meet organizational needs. This role also involves guiding team members on best practices and contributing to the ongoing enhancement of our data platforms and engineering processes.

Design, build, and maintain scalable data ingestion, transformation, and integration pipelines. Develop and optimize ETL/ELT workflows for processing diverse data types from various sources. Translate business and customer requirements into effective technical data solutions. Address and resolve complex engineering problems and non-standard data challenges. Create and maintain data models, data quality frameworks, and validation procedures. Collaborate with data analysts, data scientists, and product teams to deliver trusted data assets. Monitor and optimize pipeline performance, reliability, scalability, and cost. Support cloud-based data platforms, data warehouses, and lakehouses. Implement data governance, security, lineage, and compliance standards. Document technical designs and operational procedures. Participate in code reviews and champion engineering best practices. Troubleshoot production issues, conduct root-cause analysis, and implement lasting solutions. Contribute to automation, CI/CD, and continuous improvement initiatives within the data ecosystem.

A Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related discipline is required. Candidates should possess over 3 years of professional experience in data engineering, software engineering, or big data development. Demonstrated 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. Understanding of data modeling, data quality, and data governance principles is required. Proven analytical and problem-solving abilities are vital. Excellent communication and stakeholder collaboration skills are a prerequisite. Ability to work independently with minimal supervision and manage multiple priorities is crucial.