Senior Data Engineer, AI/ML Platforms

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer, AI/ML Platforms based in the United States. This is a senior data engineering role focused on designing and managing scalable data systems, pipelines, and data management solutions. You’ll build and maintain critical pipelines that process large volumes of data across diverse internal and external sources and platforms. The role combines hands-on engineering with data quality, governance, security, automation, and large-scale processing. You’ll partner with architects, analysts, stakeholders, and business teams to translate requirements into reliable technical solutions. You’ll have the opportunity to guide junior engineers and peers while contributing to complex, enterprise-wide data initiatives. The environment values proactive problem-solving, technical excellence, collaboration, and continuous improvement. This position is well suited to an experienced engineer who enjoys solving complex data challenges and building dependable infrastructure at scale. Collaborate with data architects, analysts, business stakeholders, and other teams to identify, clarify, and document requirements for data pipelines and process automation. Design, build, document, test, deploy, and maintain scalable and reliable data pipelines using established engineering best practices. Develop data collection and storage systems capable of processing large volumes of data across multiple platforms, sources, and data structures. Perform ETL and ELT operations in alignment with enterprise data governance, security, and compliance standards. Develop and maintain data quality and governance processes to ensure data accuracy, consistency, completeness, and reliability. Implement inspection, validation, processing, and anomaly-detection approaches to identify and mitigate data quality issues. Recommend and implement appropriate data models, storage solutions, and processing architectures based on business and technical requirements. Contribute to projects involving data collection, transformation, warehousing, integration, and process automation. Work directly with business and client stakeholders to translate business needs into practical and scalable technical solutions. Provide technical guidance, mentoring, and knowledge-sharing to junior engineers and peers. Identify potential technical issues proactively and implement measures to prevent, reduce, or resolve their impact. Create clear technical documentation covering data pipelines, processes, requirements, integrations, and operational procedures. Stay adaptable and learn new technologies, programming languages, tools, and software systems as required by evolving business needs. Requirements: 5–7 years of relevant professional experience in data engineering or a closely related technical discipline. Bachelor’s degree in a relevant field. Strong experience working with multiple software development and data engineering methodologies, including agile approaches. Proven experience designing and building large, complex data pipelines spanning multiple platforms, data sources, and data structures. Strong understanding of computer systems, integrations, databases, data storage technologies, and enterprise data environments. Hands-on experience with relational databases such as SQL Server and Oracle, including advanced SQL scripting. Experience with major cloud platforms such as AWS, Azure, or Google Cloud Platform and their associated data integration and ETL technologies. Practical expertise in data warehousing, data cleansing, transformation, sourcing, maintenance, and data lifecycle management. Strong programming experience in Python, Apache Spark, and comparable languages or frameworks. Advanced knowledge of large-scale data processing engines and distributed data processing concepts. Strong understanding of database security, data governance, compliance re