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Popular data engineer title, metro location, and broad common skill requirements increase competition.
Medium because core data engineering skills transfer broadly, but enterprise/regulatory experience increases domain specificity.
High due to explicit 8-10 years mandate and mandatory Databricks, Spark, Python, Kafka, and AWS experience.
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Design, build, and operate scalable, cloud-native data pipelines and platforms primarily on AWS using technologies like Databricks, PostgreSQL, and Aurora.
Ensure production readiness of data solutions focusing on security, testing, observability, cost efficiency, and performance optimization.
Collaborate with cross-functional teams including analysts, data scientists, and AI engineers; mentor junior engineers and contribute to technical standards.
8-10 years of hands-on data engineering experience.
Proficiency with Databricks, Python, Java, Spark, and advanced SQL.
Experience with AWS data services and cloud-native platforms, including operational experience handling production-grade data pipelines.
Work Experience Required: 8-10 years in data engineering roles.
Experienced in making architectural and data modeling decisions for enterprise-scale and complex data platforms.
Strong engineering discipline with practical production environment expertise and focus on system performance, scalability, and reliability.
Comfortable working in collaborative environments with cross-functional teams and mentoring junior engineers.