Job description

About You The ideal candidate is a problem-solver who enjoys working on complex data systems and is passionate about data quality. You thrive in collaborative environments but can also work independently to deliver solutions. You're comfortable working directly with technical and non-technical stakeholders and can communicate complex technical concepts clearly. Most importantly, you're excited about creating systems that empower others to work with data efficiently and confidently. About the Role 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. Key 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 Qualifications • 5+ years of professional experience in data engineering, with at least 2 years working on enterprise-scale data platforms • Bachelor's Degree in CS or equivalent • 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 We emphasize building systems that are maintainable, scalable, and focused on enabling self-service data access while maintaining high standards for data quality and governance. #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

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
5+ years of professional experience in data engineering, with at least 2 years working on enterprise-scale data platforms
Bachelor's Degree in CS or equivalent
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

Nice-to-haves

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
We emphasize building systems that are maintainable, scalable, and focused on enabling self-service data access while maintaining high standards for data quality and governance.

Application prep

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

Keywords to mirror

mlaici/cdkubernetessqlpython

Platform: icims
Effort: medium