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Tier-1 employer and metro location with broad data/backend requirements creates moderate applicant competition.
Core big-data and cloud engineering skills are transferable, though financial domain nuances moderately matter.
Explicit 10+ years and mandatory deep big-data, Python/Java, cloud, and platform experience raises strictness.
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Design and deliver scalable, secure data platform components including ingestion, processing, storage, and access layers to support analytics, machine learning, and applications.
Provide technical leadership and guidance influencing data platform architecture, application functionality, and technical operations.
Lead engineering practices adoption including AI-assisted development tools and automation to improve delivery speed, quality, and operational outcomes.
10+ years of applied software engineering experience with formal training or certification.
Advanced proficiency in both Python and Java with strong backend engineering skills including building APIs, services, and distributed systems.
Strong hands-on experience with big data technologies such as Spark, Databricks, Data Lake, and AWS cloud services like ECS, EKS, EMR, Lambda.
Experience designing, developing, and operating data platform pipelines and components ensuring production quality, security, and operational stability.
Experienced in leading and coaching engineering teams on responsible AI-assisted development and delivery within enterprise environments.
Skilled in independently solving design and functionality problems in large, global corporate teams with minimal oversight.
Familiar with cloud-based distributed computing and modern data engineering tools, with an emphasis on scalability, security, and governance.