Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Mid-level data engineer in metros with broad required stack and generalist title increases competition.
Technical data engineering skills are highly transferable across industries despite insurance context.
Explicit 4+ years plus many mandatory modern data stack tools makes shortlisting highly stringent.
Lead and oversee technological choices and implementation of data pipelines and data warehousing architecture.
Serve as lead or Subject Matter Expert on cross-organizational projects automating data value chain processes and promote best practices.
Design maintainable data architecture and pipelines enabling efficient access for analysts and data scientists; mentor team members and evaluate new tools for scalability.
Bachelor's or Master's degree in a technical field.
4+ years of experience in Data Engineering including data pipelining, warehousing, and ETL tools.
Hands-on expertise with Python, SQL, Snowflake, Airflow, dbt, and data engineering tooling (Jira, git, Terraform, containers, GCP or AWS).
Knowledge of ETL patterns, modern data warehousing concepts (data mesh, data vaulting), and data quality/test-driven design.
Experienced in both high-level data architecture and hands-on coding.
Able to bridge communication between data scientists and software engineers effectively.
Demonstrates leadership in owning and delivering modern, scalable data pipeline solutions, with a focus on innovation, automation, and data governance.