Job description

Job Purpose ICE Data Services provides global securities evaluations, reference data, and analytics designed to support financial institutions' and investment funds' valuation activities, securities operations, research, and portfolio management. The Enterprise Data Architecture team is looking for a Software Development Manager to lead the engineering team responsible for building and operating ICE Data Services' next-generation enterprise data platform. This leader will manage engineers focused on scalable data orchestration, self-service data pipeline capabilities, data quality, governance, and platform services that enable analytics, ML, and AI workflows across the organization. We are seeking a technically credible and highly organized engineering leader with strong people-management skills, a background in software or data engineering, and the ability to partner across architecture, infrastructure, product, governance, QA, and business stakeholder groups. This role is accountable for team execution, engineering quality, platform reliability, operational maturity, and the development of engineering talent. Responsibilities • Lead, mentor, and manage a team of software and data engineers responsible for enterprise data platform capabilities. • Translate strategic platform objectives into clear engineering roadmaps, delivery plans, milestones, and measurable outcomes. • Manage the delivery of scalable, secure, maintainable, and reliable data platform services, including orchestration, pipeline automation, testing frameworks, and reusable integration patterns. • Partner with enterprise architecture, infrastructure, product, data governance, QA, ML/AI, and business stakeholders to align platform priorities with organizational needs. • Establish and enforce engineering best practices for version control, CI/CD, automated testing, code review, observability, deployment readiness, and production support. • Drive self-service platform capabilities that allow teams across the organization to create, deploy, and operate data pipelines with appropriate guardrails and reduced dependency on centralized engineering support. • Champion data quality, metadata, lineage, schema governance, data contracts, and lifecycle management practices in partnership with governance and architecture teams. • Manage technical risk, dependencies, resource planning, release planning, and operational readiness for platform initiatives. • Recruit, hire, onboard, and retain engineering talent while supporting career development, performance management, and succession planning. • Create a team culture focused on accountability, collaboration, engineering excellence, continuous improvement, and business-impact delivery. • Serve as an escalation point for critical platform issues, operational incidents, delivery risks, and cross-team blockers. • Communicate priorities, status, risks, and outcomes clearly to senior leadership and cross-functional partners. Knowledge and Experience • Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Engineering, or a related discipline. • 8+ years of professional software engineering, data engineering, or platform engineering experience. • 2+ years of experience leading or managing engineering teams, including performance management, hiring, mentoring, and delivery accountability. • Experience managing teams responsible for enterprise-scale data platforms, distributed systems, workflow orchestration, shared platform services, or large-scale application infrastructure. • Strong understanding of modern software engineering practices, including SDLC, Agile delivery, CI/CD, automated testing, code quality, release management, and production support. • Familiarity with data engineering concepts such as pipeline orchestration, batch and real-time processing, data transformation, data quality frameworks, and testing strategies. • Experience with containerized or Kubernetes-based platform environments, infrastructure automation, monitoring, alerting, and operational reliability practices. • Working knowledge of SQL, Python, data modeling concepts, data warehouse design principles, and integration patterns for enterprise data systems. • Understanding of data governance practices, including metadata management, lineage, schema governance, data contracts, entitlement considerations, auditability, and compliance-driven controls. • Ability to work cross-functionally with architects, product managers, infrastructure teams, QA, data scientists, ML engineers, and business stakeholders. • Strong communication skills with the ability to explain complex technical topics clearly to technical and non-technical audiences. • Demonstrated ability to prioritize competing demands, manage delivery risk, make pragmatic technical decisions, and operate effectively in a fast-paced environment. • Experience with modern development tools such as Git, Jenkins or other CI/CD platforms, Jira, Confluence, and related engineering workflow systems. • Prior experience supporting AI/ML data workflows, feature engineering pipelines, streaming architectures, or self-service data platform capabilities is preferred. • Experience leading geographically distributed teams is preferred. #LI-HR1 #LI-ONSITE Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.

Requirements

Must-haves

Lead, mentor, and manage a team of software and data engineers responsible for enterprise data platform capabilities.
Translate strategic platform objectives into clear engineering roadmaps, delivery plans, milestones, and measurable outcomes.
Manage the delivery of scalable, secure, maintainable, and reliable data platform services, including orchestration, pipeline automation, testing frameworks, and reusable integration patterns.
Partner with enterprise architecture, infrastructure, product, data governance, QA, ML/AI, and business stakeholders to align platform priorities with organizational needs.
Establish and enforce engineering best practices for version control, CI/CD, automated testing, code review, observability, deployment readiness, and production support.
Drive self-service platform capabilities that allow teams across the organization to create, deploy, and operate data pipelines with appropriate guardrails and reduced dependency on centralized engineering support.
Champion data quality, metadata, lineage, schema governance, data contracts, and lifecycle management practices in partnership with governance and architecture teams.
Manage technical risk, dependencies, resource planning, release planning, and operational readiness for platform initiatives.
Recruit, hire, onboard, and retain engineering talent while supporting career development, performance management, and succession planning.
Create a team culture focused on accountability, collaboration, engineering excellence, continuous improvement, and business-impact delivery.
Serve as an escalation point for critical platform issues, operational incidents, delivery risks, and cross-team blockers.
Communicate priorities, status, risks, and outcomes clearly to senior leadership and cross-functional partners.
Knowledge and Experience
Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Engineering, or a related discipline.
8+ years of professional software engineering, data engineering, or platform engineering experience.
2+ years of experience leading or managing engineering teams, including performance management, hiring, mentoring, and delivery accountability.
Experience managing teams responsible for enterprise-scale data platforms, distributed systems, workflow orchestration, shared platform services, or large-scale application infrastructure.
Strong understanding of modern software engineering practices, including SDLC, Agile delivery, CI/CD, automated testing, code quality, release management, and production support.
Familiarity with data engineering concepts such as pipeline orchestration, batch and real-time processing, data transformation, data quality frameworks, and testing strategies.
Experience with containerized or Kubernetes-based platform environments, infrastructure automation, monitoring, alerting, and operational reliability practices.
Working knowledge of SQL, Python, data modeling concepts, data warehouse design principles, and integration patterns for enterprise data systems.
Understanding of data governance practices, including metadata management, lineage, schema governance, data contracts, entitlement considerations, auditability, and compliance-driven controls.

Nice-to-haves

Prior experience supporting AI/ML data workflows, feature engineering pipelines, streaming architectures, or self-service data platform capabilities is preferred.
Experience leading geographically distributed teams is preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaici/cdagilekubernetessqlpythonjira

Platform: icims
Effort: medium