Job Summary

Northeastern University is seeking an experienced and technically skilled Sr. Data Integration & Operations Engineer to join our team. This role is responsible for the day-to-day management, monitoring, operational support, and optimization of the university's data integration pipelines and processes. The role will oversee ETL/ELT workflows built on enterprise integration platforms, ensuring reliable data flow from a broad spectrum of university source systems into the data lakehouse and downstream point solutions used across the university.

24/7 business continuity

This role requires availability outside of traditional working hours on a rotating basis to ensure continuous operation of critical AI systems and data pipelines.

Other duties as required

This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem at the university.

Hybrid work schedule

This role is hybrid and in the office a minimum of three days a week.

Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future.

Minimum Qualifications

Data Integration Platform Experience: Hands-on experience administering and operating enterprise data integration platforms, with Informatica PowerCenter or IDMC strongly preferred.
Data Pipeline Operations: Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines.
Data Observability and Pipeline Monitoring: Experience with data observability platforms strongly preferred.
Incident Management, Performance Optimization, Change Management, Data Quality Management, Documentation and Knowledge Management, Automation Skills, DevOps Practices, Security Awareness, Collaboration Skills, Problem-solving, Compliance Knowledge, Communication Skills, Service Management.
Bachelor's degree in Computer Science, Information Technology, Data Management, or a related field.
Minimum of 4-5 years of experience in data integration, data engineering operations.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with cloud-based data lakehouse or data warehouse platforms.

Key Responsibilities & Accountabilities

Pipeline Monitoring, Observability, and Incident Management

Monitor data integration pipeline health, data freshness, volume trends, and job completion status using observability tools and dashboards.

Operational Support and Maintenance

Administer and maintain data integration platform environments (Informatica and related tools).

Performance Analysis and Optimization

Analyze integration pipeline performance metrics, identify bottlenecks, and implement tuning and optimization measures.

Documentation and Knowledge Management

Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles.

Continuous Improvement and Automation

Identify opportunities to automate repetitive operational tasks, improve pipeline reliability, and reduce manual intervention.

Job Summary

24/7 business continuity

This role requires availability outside of traditional working hours on a rotating basis to ensure continuous operation of critical AI systems and data pipelines.

Other duties as required

This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem at the university.

Hybrid work schedule

This role is hybrid and in the office a minimum of three days a week.

Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future.

Minimum Qualifications

Data Integration Platform Experience: Hands-on experience administering and operating enterprise data integration platforms, with Informatica PowerCenter or IDMC strongly preferred.
Data Pipeline Operations: Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines.
Data Observability and Pipeline Monitoring: Experience with data observability platforms strongly preferred.
Incident Management, Performance Optimization, Change Management, Data Quality Management, Documentation and Knowledge Management, Automation Skills, DevOps Practices, Security Awareness, Collaboration Skills, Problem-solving, Compliance Knowledge, Communication Skills, Service Management.
Bachelor's degree in Computer Science, Information Technology, Data Management, or a related field.
Minimum of 4-5 years of experience in data integration, data engineering operations.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with cloud-based data lakehouse or data warehouse platforms.

Key Responsibilities & Accountabilities

Pipeline Monitoring, Observability, and Incident Management

Monitor data integration pipeline health, data freshness, volume trends, and job completion status using observability tools and dashboards.

Operational Support and Maintenance

Administer and maintain data integration platform environments (Informatica and related tools).

Performance Analysis and Optimization

Analyze integration pipeline performance metrics, identify bottlenecks, and implement tuning and optimization measures.

Documentation and Knowledge Management

Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles.

Continuous Improvement and Automation

Identify opportunities to automate repetitive operational tasks, improve pipeline reliability, and reduce manual intervention.

Job Summary

24/7 business continuity

This role requires availability outside of traditional working hours on a rotating basis to ensure continuous operation of critical AI systems and data pipelines.

Other duties as required

This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem at the university.

Hybrid work schedule

This role is hybrid and in the office a minimum of three days a week.

Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future.

Minimum Qualifications

Data Integration Platform Experience: Hands-on experience administering and operating enterprise data integration platforms, with Informatica PowerCenter or IDMC strongly preferred.
Data Pipeline Operations: Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines.
Data Observability and Pipeline Monitoring: Experience with data observability platforms strongly preferred.
Incident Management, Performance Optimization, Change Management, Data Quality Management, Documentation and Knowledge Management, Automation Skills, DevOps Practices, Security Awareness, Collaboration Skills, Problem-solving, Compliance Knowledge, Communication Skills, Service Management.
Bachelor's degree in Computer Science, Information Technology, Data Management, or a related field.
Minimum of 4-5 years of experience in data integration, data engineering operations.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with cloud-based data lakehouse or data warehouse platforms.

Key Responsibilities & Accountabilities

Pipeline Monitoring, Observability, and Incident Management

Monitor data integration pipeline health, data freshness, volume trends, and job completion status using observability tools and dashboards.

Operational Support and Maintenance

Administer and maintain data integration platform environments (Informatica and related tools).

Performance Analysis and Optimization

Analyze integration pipeline performance metrics, identify bottlenecks, and implement tuning and optimization measures.

Documentation and Knowledge Management

Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles.

Continuous Improvement and Automation

Identify opportunities to automate repetitive operational tasks, improve pipeline reliability, and reduce manual intervention.

Job Summary

24/7 business continuity

This role requires availability outside of traditional working hours on a rotating basis to ensure continuous operation of critical AI systems and data pipelines.

Other duties as required

This role requires flexibility in performing duties outside of the primary responsibilities to support the evolving AI ecosystem at the university.

Hybrid work schedule

This role is hybrid and in the office a minimum of three days a week.

Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future.

Minimum Qualifications

Data Integration Platform Experience: Hands-on experience administering and operating enterprise data integration platforms, with Informatica PowerCenter or IDMC strongly preferred.
Data Pipeline Operations: Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines.
Data Observability and Pipeline Monitoring: Experience with data observability platforms strongly preferred.
Incident Management, Performance Optimization, Change Management, Data Quality Management, Documentation and Knowledge Management, Automation Skills, DevOps Practices, Security Awareness, Collaboration Skills, Problem-solving, Compliance Knowledge, Communication Skills, Service Management.
Bachelor's degree in Computer Science, Information Technology, Data Management, or a related field.
Minimum of 4-5 years of experience in data integration, data engineering operations.
Experience with cloud platforms (AWS, Azure, or GCP) and familiarity with cloud-based data lakehouse or data warehouse platforms.

Key Responsibilities & Accountabilities

Pipeline Monitoring, Observability, and Incident Management

Monitor data integration pipeline health, data freshness, volume trends, and job completion status using observability tools and dashboards.

Operational Support and Maintenance

Administer and maintain data integration platform environments (Informatica and related tools).

Performance Analysis and Optimization

Analyze integration pipeline performance metrics, identify bottlenecks, and implement tuning and optimization measures.

Documentation and Knowledge Management

Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles.

Continuous Improvement and Automation

Identify opportunities to automate repetitive operational tasks, improve pipeline reliability, and reduce manual intervention.

Senior Data Integration Operations Engineerr

Northeastern University

360 Huntington Ave, Boston, MA 02115, USA