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Popular data engineer title, mid-level experience, metro locations, and broad tech requirements increase candidate competition.
Core data engineering skills transfer across industries, reducing background sensitivity despite insurance domain.
Mandatory 4+ years plus multiple required technologies and cloud/tooling increases shortlisting strictness.
Lead design and implementation of data pipelines and warehousing architectures ensuring simplicity, maintainability, and efficiency.
Serve as technical lead and SME on cross-organizational projects automating data value chain processes and promoting best practices across the data organization.
Mentor data team members on architecture and coding, ensure data governance, security and reliability while championing new engineering 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.
Proficient in Python and SQL, with strong knowledge of Snowflake, Airflow, and dbt.
Experience with data engineering tools such as Jira, git, buildkite, Terraform, containers, and cloud platforms like GCP and AWS.
Experienced both architecting data systems at a high level and hands-on coding for implementation.
Comfortable working cross-functionally with data scientists, engineers, and stakeholders to translate data concepts into production.
Values ownership of end-to-end data pipeline delivery and fostering a culture of learning and innovation in data engineering.