Data Engineer Remote, United States

We are seeking a Data Engineer to design, build, and scale data infrastructure powering AI-driven products and research initiatives. You will develop distributed data pipelines, manage large-scale datasets across cloud environments, and design reliable data systems to support data processing, experimentation, and model development at scale.

Key Responsibilities - Design, build, and maintain scalable data pipelines to ingest, process, and transform large-scale datasets from multiple sources. - Develop and optimize distributed data processing workflows using Apache Spark and cloud-native technologies. - Build and maintain data storage solutions across SQL and NoSQL systems, ensuring scalability, performance, and reliability. - Design and implement data architectures on AWS to support high-volume data ingestion, processing, and distribution. - Write efficient Python and SQL code to extract, transform, validate, and analyze large datasets. - Ensure data quality, integrity, monitoring, and operational reliability across data pipelines and storage layers. - Collaborate with AI researchers, data scientists, and engineering teams to support data-intensive applications and experimentation. - Implement automation, orchestration, and monitoring workflows to support scalable and efficient data operations.

Required Skills and Qualifications - Strong proficiency in Python, SQL, and distributed data processing frameworks such as Apache Spark. - Hands-on experience with AWS data services and cloud-native data architectures. - Experie

Data Engineer Remote, United States

We are seeking a Data Engineer to design, build, and scale data infrastructure powering AI-driven products and research initiatives. You will develop distributed data pipelines, manage large-scale datasets across cloud environments, and design reliable data systems to support data processing, experimentation, and model development at scale.

Key Responsibilities - Design, build, and maintain scalable data pipelines to ingest, process, and transform large-scale datasets from multiple sources. - Develop and optimize distributed data processing workflows using Apache Spark and cloud-native technologies. - Build and maintain data storage solutions across SQL and NoSQL systems, ensuring scalability, performance, and reliability. - Design and implement data architectures on AWS to support high-volume data ingestion, processing, and distribution. - Write efficient Python and SQL code to extract, transform, validate, and analyze large datasets. - Ensure data quality, integrity, monitoring, and operational reliability across data pipelines and storage layers. - Collaborate with AI researchers, data scientists, and engineering teams to support data-intensive applications and experimentation. - Implement automation, orchestration, and monitoring workflows to support scalable and efficient data operations.

Required Skills and Qualifications - Strong proficiency in Python, SQL, and distributed data processing frameworks such as Apache Spark. - Hands-on experience with AWS data services and cloud-native data architectures. - Experie