The Lead DevOps Engineer will mentor AppOps teams, define reliability standards, and manage incident response for critical enterprise applications. They will also architect observability solutions, automate operational workflows, and partner with cross-functional teams to ensure system performance and security.
Develop and maintain scalable data pipelines using SQL-based ELT patterns and optimize transformation logic for curated data marts. Ensure data quality through rigorous testing, orchestration with Airflow, and effective schema evolution using CI/CD practices.
Design and develop high-volume batch and streaming data ingestion pipelines across AWS and GCP platforms. Lead and mentor junior engineers while collaborating with cross-functional teams to build new product features.
Collaborate with stakeholders to translate complex business processes into structured, executable instructions for AI agents. Design, implement, and maintain reusable Claude Code skills while providing technical leadership and defining platform-level standards.
Perform both manual and automated testing while designing and maintaining automated test cases for regression and new features. Collaborate with cross-functional teams including developers and business analysts to ensure high product quality and smooth release cycles.
You will be responsible for hands-on development, automation, and improving the performance, scalability, and reliability of applications. Additionally, you will manage database optimization, containerized environments, and CI/CD pipelines within an AWS stack.
Analyze marketing workflows to design and integrate scalable AI-enabled business solutions and Generative AI tools into the MarTech ecosystem. Collaborate with cross-functional engineering and security teams to ensure AI services meet enterprise standards for scalability, governance, and performance.
Design and develop scalable backend applications and microservices using Java, Spring Boot, and Quarkus. Implement event-driven solutions on AWS and enhance observability practices within an Agile environment.
Collaborate with product management to review requirements and author technical designs and architectures. Contribute to the full SDLC, including code reviews, performance validation, and the improvement of CI/CD frameworks.
Assess and document the current Martech ecosystem to identify redundancies and design a simplified, scalable target-state architecture. Collaborate with cross-functional stakeholders to translate business requirements into practical architecture decisions and implementation plans for AI-driven initiatives.
Build and maintain core platform components including SSO, networking, and CI/CD pipelines while managing AWS landing zones and container orchestration. Design platform 'golden paths' and provide self-service tools to enable fast, secure deployments for distributed teams.
Assess and document the current Martech ecosystem to identify redundancies and design a simplified, scalable target-state architecture. Collaborate with cross-functional stakeholders to translate business requirements into practical architecture decisions and implementation plans.
Design and maintain scalable ELT pipelines and modern data architectures using dbt and SQL. Collaborate with cross-functional teams to support data-driven applications and manage infrastructure as code via Terraform.
Design and maintain enterprise-grade Java applications and customize Murex components for Capital Markets platforms. Develop real-time and batch integrations while leveraging AI-assisted tools to optimize productivity and code quality.
Act as a liaison between front-office users and technical teams to gather and analyze business requirements for Murex modules. Support the implementation, configuration, and optimization of trading workflows and financial product structures.
Design and evolve cloud-native, service-oriented architectures on Microsoft Azure, integrating AI and Generative AI capabilities. Provide technical leadership on cloud modernization, legacy migration, and the implementation of scalable architectural patterns.
The Lead Data Engineer will assess data sources, quality, and integration requirements to validate the technical feasibility of AI and reporting use cases. They will provide platform recommendations, identify implementation risks, and support technical documentation for final recommendations.
The Lead Data Engineer will assess data sources and availability to identify ingestion and integration requirements for AI and reporting use cases. They will validate technical feasibility, identify implementation risks, and provide technical documentation and sizing for build activities.
Design, build, and optimize ETL/ELT pipelines to transform data into actionable datasets for business strategy. Maintain data lakes and feature stores to support the ML Engineering team in developing subrogation solutions.
Serve as a subject matter expert in commodities trading to support front-office workflows and operational processes. Collaborate with traders and technology teams to gather requirements, document functional specifications, and drive system enhancements.
Design and evaluate A/B tests and experimentation frameworks to generate actionable product insights. Build dashboards and reporting tools to support data-driven decision making and OKR tracking.
Serve as a subject matter expert in commodities trading to support front-office workflows and operational processes. Collaborate with trading desks and technology teams to gather requirements, document functional specifications, and drive system enhancements.
