As a Lead Associate in Data Engineering, you will architect, build, and maintain robust data pipelines and platforms. This role is crucial for empowering analytics, reporting, and AI/ML initiatives.

You will tackle moderately complex data engineering challenges independently, collaborating closely with business and technology stakeholders to translate requirements into effective data solutions. This position also involves providing technical guidance to team members, promoting best practices, and driving continuous improvements in our data platforms and engineering processes.

Key responsibilities include designing, developing, and maintaining scalable data ingestion, transformation, and integration pipelines. You will build and optimize ETL/ELT workflows, analyze business requirements, and develop technical data solutions. Identifying and resolving complex engineering problems, developing data models, and ensuring data quality are also core functions. Collaboration with various 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, implementing data governance and security standards, and creating technical documentation. Participation in code reviews, troubleshooting production issues, and contributing to automation and CI/CD processes are expected. Adherence to company policies and directives is also a requirement.

A Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field is required. Candidates should possess over 3 years of experience in data engineering, software engineering, or big data development, with a strong background in building ETL/ELT pipelines and working with large datasets. Proficiency in SQL and Python for data processing, along with knowledge of data warehouses, data lakes, and modern data architectures, is essential.

Familiarity with cloud platforms like Azure, AWS, or Google Cloud, and an understanding of data modeling, data quality, and data governance principles are necessary. Strong analytical and problem-solving skills, coupled with excellent communication and stakeholder collaboration abilities, are vital. The ability to work independently with minimal supervision and manage multiple priorities is also a must.