Job Purpose
We're seeking a talented Senior Data Engineer to join our Enterprise Architecture team in a cross-cutting role that will help define and implement our next-generation data platform. In this pivotal position, you'll lead the design and implementation of scalable, self-service data pipelines with a strong emphasis on data quality and governance. This is an opportunity to shape our data engineering practice from the ground up, working directly with key stakeholders to build mission-critical ML and AI data workflows.
Responsibilities
Design, build, and maintain our on-premises data orchestration platform using the best-in-breed open source tools
Create self-service capabilities that empower teams across the organization to build and deploy data pipelines without extensive engineering support
Implement robust data quality testing frameworks that ensure data integrity throughout the entire data lifecycle
Establish data engineering best practices, including version control, CI/CD for data pipelines, and automated testing
Collaborate with ML/AI teams to build scalable feature engineering pipelines that support both batch and real-time data processing
Develop reusable patterns for common data integration scenarios that can be leveraged across the organization
Work closely with infrastructure teams to optimize our Kubernetes-based data platform for performance and reliability
Mentor junior engineers and advocate for engineering excellence in data practices
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Knowledge and Experience
At least a Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Mathematics, Engineering, or related disciplines.
5+ years of professional experience in data engineering, with at least 2 years working on enterprise-scale data platforms
Deep expertise with orchestrating workflows, performance optimization, and operational management
Strong understanding of data transformation techniques, including experience with testing frameworks and deployment strategies
Experience with stream processing frameworks and technologies
Proficiency with SQL and Python for data transformation and pipeline development
Familiarity with containerized application deployment
Experience implementing data quality frameworks and automated testing for data pipelines
Ability to work cross-functionally with data scientists, ML engineers, and business stakeholders
Preferred Qualifications
Experience with self-hosted data orchestration platforms (rather than managed services)
Background in implementing data contracts or schema governance
Knowledge of ML/AI data pipeline requirements and feature engineering
Experience with real-time data processing and streaming architectures
Familiarity with data modeling and warehouse design principles
Prior experience in a technical leadership role
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- Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.
