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Mid-level data role, popular title, metro locations and broad stack create high candidate competition.
Core data engineering skills transfer across industries, though insurance domain familiarity slightly increases fit sensitivity.
Explicit 4+ years plus many mandatory tools (Snowflake, Airflow, dbt, GCP, Terraform) increases strictness.
Lead design and implementation of data pipelines and warehouse architecture ensuring simplicity, maintainability, and efficiency for analysts and data scientists.
Act as technical SME and lead cross-organizational projects automating data processes while enforcing data governance and reliability.
Mentor data team members, promote best practices, and evaluate new engineering tools to scale team capabilities.
Bachelor's or Master's degree in a technical discipline.
4+ years of experience in data engineering including data pipelining, warehousing, and ETL tools.
Proficiency with Python, SQL, Snowflake, Airflow, dbt and familiarity with GCP suite, Terraform, Kubernetes, and containers.
Experience with data engineering tooling such as Jira, git, buildkite, Terraform; knowledge of ETL patterns, data warehousing concepts like data mesh and vaulting; and data quality/test-driven design.
Strong hands-on experience balancing high-level architecture and detailed coding in data engineering contexts.
Experienced in working cross-functionally with data scientists and engineers to operationalize data concepts.
Capable of mentoring and promoting continuous learning and innovative problem-solving within a data organization.