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Mid-senior generalist data engineer title, known employer and metro hiring drive moderate competition.
Core data engineering skills are highly transferable across industries despite insurance context.
Explicit 6-9 years requirement plus mandatory data engineering and cloud experience increases screening strictness.
Build and deliver small to medium scale data pipelines and products using ELT solutions across multiple platforms and technologies.
Implement continuous integration and delivery capabilities following Enterprise DevOps practices and data engineering standards including source code management and issue tracking.
Research and apply emerging big data technologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake, Talend) in cloud/on-premise hybrid environments; support project and portfolio strategy development.
6 to 9 years of software development or data engineering experience demonstrating best practices in programming, SDLC, and distributed systems.
At least 1 year of experience developing and operating production workloads on cloud infrastructure.
Strong knowledge of cloud data pipelines, APIs, DevOps technology stacks, and agile/iterative methodologies.
Strong written and verbal communication skills; ability to work independently and collaborate with cross-functional teams.
Experienced in building and operating data solutions in hybrid cloud environments (AWS and on-premises) with familiarity in big data frameworks and cloud data warehousing.
Skilled in applying DevOps practices, continuous integration/delivery, and quality/code management within lean and fast-paced agile teams.
Able to communicate effectively with technical and non-technical stakeholders and influence solution design and technology decisions.