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Mid-level Data Engineer role, common tech stack, and metro locations create high applicant competition.
Data engineering skills (Python, SQL, ETL, cloud) are broadly transferable across industries.
Explicit 4+ years and mandatory Snowflake, Airflow, dbt, GCP, Terraform, Kubernetes increase selection strictness.
Own technological choices and implementation of data pipelines and warehousing philosophy.
Lead and execute cross-organizational projects automating data value chain processes and promote technical best practices within the data organization.
Design maintainable data architecture and mentor team members while ensuring data pipeline reliability, governance, and security.
4+ years of experience in Data Engineering including Data Pipelining, Warehousing, and ETL tools.
Bachelor's or Master's degree in a technical field.
Strong hands-on experience with Python, SQL, Snowflake, Airflow, and dbt.
Experience with data engineering tools such as Jira, git, buildkite, Terraform, containers, GCP, Kubernetes, and cloud functions.
Experienced in both high-level data architecture and low-level coding, capable of bridging data science and software engineering.
Proven ability to lead a team and take ownership of delivering modern, efficient data pipeline components.
Strong understanding of ETL patterns, modern data warehousing concepts (data mesh, data vaulting), and data quality practices including test-driven design and data observability.