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Specialized lakehouse skillset and seniority moderate applicant competition despite metro location.
High due to specialized lakehouse, governance, and cloud data engineering requirements limiting transferability.
Explicit 10+ years and 5+ years leading lakehouse projects plus mandatory cloud/devops skills.
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Design, build, and maintain scalable data lake and lakehouse architectures using AWS (S3, Glue, Lake Formation), Snowflake, and related cloud-native services.
Develop and optimize end-to-end batch and streaming data pipelines for diverse structured and unstructured data sources enabling analytics, reporting, and AI-driven products.
Lead data governance, cataloging, lineage, and access control implementation while collaborating with cross-functional teams to deliver reliable and compliant data products.
10+ years in Data Engineering, Software Engineering, and/or Cloud Engineering with 5+ years leading data lake or lakehouse transformation on AWS.
Bachelor's degree in Computer Science, Data Science, or related field; Master's preferred.
Strong experience with AWS data lake services (S3, Glue, Lake Formation), Snowflake, Delta Lake, and data governance tools (Unity Catalog, Collibra, Atlan).
Proficient in Python/SQL for production data solutions; pipeline orchestration tools like Airflow, Step Functions; and DevOps practices (Terraform/CloudFormation, CI/CD).
Senior engineer with hands-on expertise driving large-scale AWS-based lakehouse transformations and modern data engineering frameworks (dbt, Airflow).
Experienced in implementing strong data governance, security, and compliance (PII, GDPR, HIPAA) across data platforms.
Able to lead and mentor teams, coordinate with multiple stakeholders, and incorporate AI/ML augmentation tools to enhance productivity and innovation.