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Senior Data Engineer

Atlanta, Georgia

Pay
Pay not listedSimilar roles state $127K–$190K
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting

What you’ll work on

Full posting
  • Design, build and maintain an on-premises data orchestration platform using open source tools

  • Create self-service capabilities so teams can build and deploy data pipelines

  • Implement data quality testing frameworks across the data lifecycle

  • Build feature engineering pipelines for batch and real-time ML/AI data

  • Mentor junior engineers and promote data engineering best practices

From the employer’s posting
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
Collaborate with ML/AI teams to build scalable feature engineering pipelines that support both batch and real-time data processing
Mentor junior engineers and advocate for engineering excellence in data practices

What you’ll bring

All qualifications

Preferred experience

  • Experience with self-hosted data orchestration platforms (rather than managed services)
  • 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
Qualification wording
Experience with self-hosted data orchestration platforms (rather than managed services)
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
Education & alternatives
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

Tools in this posting

  • Python
  • SQL
  • Kubernetes
Source — Tool mentions in context
- Experience with stream processing frameworks and technologies - Proficiency with SQL and Python for data transformation and pipeline development - Familiarity with containerized application deployment
- 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

Posting record

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Status in our records
Live
First seen by us
Sep 12, 2026 · 48 days ago
Recorded sightings
213
Last seen by us
Oct 31, 2026

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Job description

View original posting ↗

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

 

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

 

#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.
Source details

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Location & working pattern

Atlanta, Georgia

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