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Mid-level data engineer in a metro with broad cloud and GenAI requirements increases applicant competition.
Core data engineering and cloud skills are highly transferable across industries.
Explicit 6+ years requirement plus many mandatory data, cloud, and Generative AI technologies.
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Design, develop, and maintain scalable data platforms including data lakes, warehouses, and AI-enabled applications for enterprise analytics and Generative AI use cases.
Build and optimize batch and real-time data pipelines using Python, Spark, Kafka, Airflow, and AWS cloud-native services ensuring high performance at scale.
Develop and integrate Generative AI solutions leveraging Large Language Models, RAG, vector databases, AI agents, and manage cloud deployment with DevOps and MLOps best practices.
Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, Engineering, or related field.
6+ years of experience in Data Engineering, Big Data, Analytics, and/or AI application development.
Proficiency with Python, SQL, Spark, AWS Cloud services (S3, Redshift, Glue, EMR, Lambda, Bedrock, SageMaker), containerization (Docker, Kubernetes), and CI/CD pipelines.
Experience with Generative AI technologies including LLMs, Retrieval-Augmented Generation, vector databases, AI agents, and prompt engineering.
Has strong expertise in building scalable, cloud-native data and AI platforms with emphasis on enterprise-grade security, governance, and compliance.
Experienced in collaboration with business stakeholders, data scientists, and AI engineers translating requirements into scalable AI/data solutions.
Comfortable working at hyperscale environments solving complex data engineering challenges integrating modern big data and Generative AI technologies.