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Tier-1 brand, metro location, mid-level data role and broad cloud-data requirements increase candidate competition.
Core data engineering skills are transferable, though banking compliance and responsible-AI needs increase sensitivity.
Mandatory 5+ years and specific cloud-data, Python, and production-scale platform experience create strict screening.
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Lead design and delivery of scalable, secure cloud-native data platforms and data-intensive applications supporting Research Technology business goals.
Provide technical leadership, mentorship, and direction including adoption of AI-assisted engineering practices to improve code quality and delivery.
Build modern data pipelines and solutions, influence product design, architecture, and operations within an agile team environment.
5+ years of applied software engineering experience with formal training or certification.
Hands-on experience with large-scale cloud-native data platforms and modern data engineering technologies (e.g., ETL, Glue, S3, Athena, Redshift, Snowflake).
Proficiency in Python and/or Java, including developing APIs and backend services.
Experience leading AI-assisted software development tools usage, responsible AI practices, and system design for microservices and distributed systems.
Experienced in architecting and developing production-scale cloud-native data engineering solutions in commercial environments, preferably on AWS.
Demonstrates ability to drive adoption of advanced automation including AI-assisted development tools to improve operational outcomes.
Effective communicator who can translate complex technical design decisions to varied stakeholders and provide mentorship within technical teams.