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Mid-level, metro location, and generalist Data Engineer title increase applicant competition despite lakehouse specialization.
Core data engineering skills are highly transferable across industries despite specific lakehouse tooling.
Explicit 6–10 years plus mandatory Iceberg, AWS, streaming, governance, and CI/CD skills enforce strict filters.
Design and build scalable, reusable data pipelines and curated datasets on AWS-based lakehouse architecture using batch and streaming data processing.
Develop and optimize open table format datasets (e.g., Apache Iceberg) ensuring interoperability across multiple query engines and cloud providers.
Implement automated data quality checks, metadata, lineage integration, and champion CI/CD, observability, and governance best practices for data pipelines.
6 to 10 years of experience in data engineering or related roles.
Proficiency in Python and SQL.
Experience with AWS-native services (e.g., S3, Glue, Kinesis) and orchestration tools like Airflow.
Work Experience Required: 6 to 10 years in data engineering or related roles (explicitly mentioned).
Experienced in delivering production-ready data products with a focus on batch and streaming data.
Strong expertise in open standards and cloud-agnostic data engineering, especially with Apache Iceberg or similar technologies.
Able to provide technical leadership, mentor junior engineers, and collaborate cross-functionally with platform engineers and governance teams.