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Senior, specialized title but metro location and broad cloud/AI skillset increase applicant density to medium.
Core data engineering skills transfer across industries, but enterprise-scale platform experience raises domain specificity.
Explicit 10+ years and many mandatory cloud, Snowflake, and CI/CD/IaC skills make shortlisting highly strict.
Design, develop, and optimize scalable, reliable data pipelines and cloud-native data platforms using Python, SQL, Snowflake, and cloud technologies (AWS/Azure/GCP).
Lead architecture decisions, establish engineering best practices, mentor senior engineers, and drive adoption of AI-enabled data solutions including ML pipelines and LLM integrations.
Implement and manage CI/CD pipelines, Infrastructure as Code, and ensure high data quality, governance, security, and performance optimization across data engineering workflows.
10+ years of experience in Data Engineering.
Strong proficiency in Python programming, advanced SQL, and experience with Snowflake or equivalent cloud data warehouse technology.
Experience working with cloud platforms Microsoft Azure (preferred), AWS, or Google Cloud Platform and CI/CD tools like Azure DevOps, GitHub Actions, Jenkins, or GitLab CI/CD.
Knowledge of DevOps practices including Docker, Kubernetes, and Infrastructure as Code (Terraform, CloudFormation).
Experienced technical leader capable of architectural ownership and mentoring senior engineering teams in enterprise-scale data platform delivery.
Strong background in cloud-native data engineering with proven skills in modern data architectures including data lakes, lakehouse, streaming, and AI/ML pipelines.
Demonstrates deep expertise in integrating AI/ML solutions such as generative AI, LLMs, and vector databases into data engineering platforms.