Join our dynamic team as a Lead Associate Data Engineer, focusing on designing, developing, and maintaining robust data pipelines and platforms. This role is crucial for enabling advanced analytics, comprehensive reporting, and cutting-edge AI/ML solutions. You will tackle moderately complex data engineering challenges independently, collaborate closely with business and technology stakeholders to grasp intricate requirements, and architect reliable data solutions that cater to enterprise-wide data needs. As a key technical resource, you'll offer guidance on best practices and drive the continuous enhancement of our data platforms and engineering processes.

Key responsibilities include designing and developing scalable data ingestion, transformation, and integration pipelines. You will build and optimize ETL/ELT workflows for diverse data types and sources, translating business requirements into effective technical data solutions. Identifying and resolving complex engineering problems, developing data models, and ensuring data quality are also core functions. Collaboration with data analysts, scientists, and product teams to deliver trusted data assets, alongside monitoring and optimizing pipeline performance, reliability, and cost-efficiency, is essential.

Further duties involve supporting cloud-based data platforms and data warehouses/lakehouses, implementing data governance, security, and compliance standards, and creating detailed technical documentation. Participation in code reviews, troubleshooting production issues with root-cause analysis, and contributing to automation and CI/CD initiatives are also expected. Adherence to employment contracts, company policies, and directives is mandatory.

A Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field is required, alongside a minimum of 3 years of experience in data engineering, software engineering, or big data development. Proven experience in developing ETL/ELT pipelines and managing large-scale datasets is essential.

Candidates should possess experience with data warehouses, data lakes, and modern data architectures. Strong proficiency in SQL and Python for data processing is a must. Familiarity with cloud data platforms like Azure, AWS, or Google Cloud, along with a solid understanding of data modeling, data quality, and data governance principles, is necessary. Excellent analytical, problem-solving, communication, and stakeholder collaboration skills are crucial. The ability to work independently and manage multiple priorities is also vital.