Data Engineer IV

A technology-driven organization is seeking a Data Engineer IV to lead the development and management of large-scale data pipelines and data warehousing solutions. The role involves guiding technical efforts, establishing infrastructure and security standards, and implementing scalable automation to enhance analytical capabilities. The ideal candidate will have strong leadership skills in data engineering, a focus on data quality and resiliency, and experience working in a collaborative, hybrid environment.

A technology-driven organization is seeking a Data Engineer IV to lead the development and management of large-scale data pipelines and data warehousing solutions. The role involves guiding technical efforts, establishing infrastructure and security standards, and implementing scalable automation to enhance analytical capabilities. The ideal candidate will have strong leadership skills in data engineering, a focus on data quality and resiliency, and experience working in a collaborative, hybrid environment.

• Lead hands-on data development efforts, providing technical direction, code reviews, and engineering oversight for multiple initiatives.
• Establish development environments, infrastructure standards, security controls, and migration strategies across multiple cloud accounts and environments.
• Architect, develop, and oversee enterprise-scale data pipelines, ETL processes, data ingestion frameworks, and orchestration workflows.
• Identify data gaps, define strategic remediation plans, and implement automation solutions to improve organizational analytics.
• Design and implement highly reliable data pipelines with an emphasis on data quality, observability, resiliency, and operational support.
• Build and optimize large-scale data warehousing solutions that enhance both user experience and system performance.
• Drive technical decision-making, influence architectural direction, and communicate complex technical concepts to diverse stakeholders.
• Mentor engineers, foster technical growth, and build high-performing data engineering teams.
• Lead cross-functional initiatives involving data engineering, analytics, architecture, platform engineering, and business teams.
• Manage competing priorities effectively within a fast-paced enterprise environment.

• Ability to lead hands-on development while providing technical direction and oversight.
• Experience establishing development environments, infrastructure standards, security controls, and migration strategies across multiple cloud environments.
• Proficiency in architecting, developing, and governing enterprise-scale data pipelines and workflows.
• Proven ability to identify data gaps and implement scalable automation solutions.
• Skilled in designing reliable data pipelines emphasizing quality, observability, resiliency, and supportability.
• Experience with large-scale data warehousing solutions focusing on performance and user experience.
• Strong ability to communicate technical concepts to technical and non-technical audiences.
• Demonstrated mentoring experience and leadership in high-performance data engineering teams.
• Experience leading cross-functional initiatives involving data, analytics, architecture, and platform engineering.
• Ability to manage multiple priorities in a dynamic environment.
• Relevant certifications such as AWS Certified Data Engineer or AWS Solutions Architect are preferred.

• Hybrid work environment in Charlotte, NC.
• Experience with enterprise data governance and metadata management platforms.
• Knowledge of real-time analytics, event-driven architectures, and streaming data platforms.
• Familiarity with modern data architecture patterns including Data Mesh, Lakehouse, and domain-oriented design.
• Leadership experience in large-scale cloud transformation or data modernization programs.
• Experience creating AI applications using Generative AI platforms.
• Building AI-ready data pipelines and machine learning workflows, including feature engineering and MLOps.
• Proficiency in Python, SQL, Spark, and Generative AI technologies.
• Hands-on experience with cloud platforms including AWS, Azure, and Databricks.
• Knowledge of language models, Retrieval-Augmented Generation (RAG), vector databases, and AI-powered search.
• Strong understanding of responsible AI, data governance, data quality, security, and AI compliance practices.

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