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Metro mid-level data engineer role with broad GenAI and cloud requirements increases competition significantly.
Requires specialized data engineering and GenAI expertise, limiting easy transfer across unrelated industries.
Explicit 6+ years requirement plus many mandatory tech stack and cloud/GenAI skills creates high filter strictness.
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Design, develop, and maintain scalable data pipelines, data lakes, data warehouses, and AI-enabled data applications to support enterprise analytics and Generative AI use cases.
Build and optimize batch and real-time data pipelines with Python, Spark, Kafka, Airflow, and cloud-native services for high-performance data processing at scale.
Develop and integrate Generative AI solutions using LLMs, Retrieval-Augmented Generation, vector databases, AI agents, and manage deployment on AWS with DevOps/MLOps best practices.
Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, Engineering, or a related field.
6+ years of experience in Data Engineering, Big Data, Analytics, or AI application development.
Strong experience with Python, SQL, Spark, cloud services (mainly AWS S3, Redshift, Glue, EMR, Lambda, Bedrock, SageMaker), containerization (Docker, Kubernetes), and CI/CD pipelines.
Experience with Generative AI technologies including Large Language Models, Retrieval-Augmented Generation, vector databases, and prompt engineering.
Experienced in building scalable, cloud-native data platforms and AI-driven applications in enterprise environments using modern big data and cloud technologies.
Skilled in integrating complex data pipelines with AI and analytics teams, aligning technical implementations with evolving business requirements.
Proficient in deploying and managing Generative AI solutions within highly automated CI/CD environments, ensuring security, governance, and performance at scale.