What you'll be doing

Execute creative software solutions, design, development, and technical troubleshooting to solve complex problems
Develop secure, high-quality production code for data-intensive applications and review code written by others
Identify opportunities to automate remediation of recurring issues and improve operational stability
Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials
Drive adoption and governance of approved AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
Apply knowledge of tools within the Software Development Life Cycle toolchain, including AI-assisted development and automation
Drive communities of practice across Software Engineering to promote new and leading-edge technologies
Foster a team culture of diversity, opportunity, inclusion, and respect

Requirements

Proficiency in Engineering & Architecture, AI/ML, with experience designing, implementing, testing, and ensuring operational stability of large-scale enterprise data platforms
Advanced skills in one or more programming languages such as Java, Python, C/C++, or C#
Practical experience in system design, application development, testing, and operational stability
Working knowledge of relational and NoSQL databases and data lake architectures
Experience developing, debugging, and maintaining code with modern programming languages and database querying languages
Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, Observability tools (Dynatrace, Splunk, Grafana), and Orchestration tools (Airflow, Temporal)
Demonstrated experience leading enterprise-authorized AI-assisted software development tools and setting team expectations for validation
Strong understanding of responsible AI use in engineering workflows including data sensitivity, secure handling of inputs/outputs, and compliance coaching
Proficiency in automation, continuous delivery methods, and all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies, CI/CD, application resiliency, and security
Practical cloud-native experience

Experience with modern data technologies such as Databricks or Snowflake
Hands-on experience with Spark/PySpark and other big data processing technologies
Proficiency in software applications and technical processes within data engineering, cloud, AI, machine learning, or mobile
Knowledge of the financial services industry and their IT systems