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Strong Tier-1 brand plus generalist data engineering skills create moderate applicant competition.
Core data engineering skills transfer well, but finance domain preference raises background sensitivity to medium.
Requires production Python, AWS and strong DB skills but lacks explicit years, so moderately strict.
Develop and maintain production-grade software services, APIs, and tools primarily in Python, ensuring code quality, testing, and maintainability.
Design and implement software components end-to-end on AWS infrastructure (Lambda, S3, containers) that process large-scale financial data and manage database schema and integration.
Own feature delivery by debugging, fixing, and supporting production software while collaborating with product and business teams and contributing to architecture and engineering standards.
Proven experience building and shipping real software applications or services (not just scripts or notebooks).
Strong software engineering skills with clean, maintainable, and testable code, preferably in Python or another serious general-purpose language (Java, C#, C, C++, Scala, Go, Rust) with motivation to work in Python.
Experience with relational databases (e.g., Aurora PostgreSQL) including schema design and query optimization.
Practical experience with AWS cloud services (S3, Lambda, containers) and Git-based workflows including code reviews and CI/CD processes.
Experienced software engineer comfortable working hands-on in Python or another general-purpose language, with the ability to learn new languages quickly.
Developer with solid understanding of data-intensive software, including batch processing, ETL, and schema evolution, ideally in a cloud environment.
Engineer who can work closely with business and product teams to deliver impactful software in an Agile setting, taking end-to-end ownership and contributing to engineering best practices and standards.