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Senior Data Integration Operations Engineer

Job in Boston, Suffolk County, Massachusetts, 02298, USA
Listing for: The Chronicle Of Higher Education, Inc.
Full Time position
Listed on 2026-07-20
Job specializations:
  • IT/Tech
    Data Engineering
Salary/Wage Range or Industry Benchmark: 120000 - 190000 USD Yearly USD 120000.00 190000.00 YEAR
Job Description & How to Apply Below

Senior Data Integration Operations Engineer

Summary:

Northeastern University seeks an experienced Sr. Data Integration & Operations Engineer to manage daily data integration pipelines and processes. The role oversees ETL/ELT workflows on enterprise platforms, ensuring reliable data flow from source systems into the data lakehouse and downstream solutions. Requires hands-on expertise in data integration platform administration, pipeline operations, data observability, incident management, and continuous improvement in production environments.

Key Responsibilities & Accountabilities
  • Pipeline Monitoring, Observability, and Incident Management
    :
    Monitor pipeline health, data freshness, volume, and job completion using observability tools. Detect and resolve incidents, coordinate with source system owners and technical teams, and ensure timely recovery to minimize impact.
  • Operational Support and Maintenance
    :
    Administer and maintain data integration platform environments (Informatica and related tools), including job scheduling, connector configuration, data refreshes, and platform patching. Manage integration jobs feeding the data lakehouse and downstream solutions. Schedule maintenance with minimal disruption and manage user access per security policies.
  • Performance Analysis and Optimization
    :
    Analyze performance metrics, identify bottlenecks and long-running jobs, implement tuning. Contribute to the data observability platform strategy, metrics, SLAs, and alert rules.
  • Documentation and Knowledge Management
    :
    Create and maintain operational documentation, runbooks, SOPs, and knowledge articles. Document system configurations, data pipeline dependencies, and recovery procedures.
  • Continuous Improvement and Automation
    :
    Identify opportunities to automate repetitive tasks, improve reliability, and reduce manual intervention. Develop scripts and workflows; evaluate tools (including Fivetran) and evolve data integration practices.
  • Position Type
    :
    Information Technology
  • Additional Information
    :
    Northeastern University offers comprehensive benefits for benefit-eligible employees. See Northeastern HR benefits information for details. All qualified applicants are encouraged to apply and will be considered without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other protected characteristic. Compensation ranges and hiring details are provided by the university.
Minimum Qualifications
  • Data Integration Platform Experience
    :
    Hands-on experience administering and operating enterprise data integration platforms, with Informatica Power Center or IDMC strongly preferred. Experience with SaaS-based ELT tools such as Fivetran is a plus. Ability to manage complex integration workflows, configure connectors, and troubleshoot end-to-end.
  • Data Pipeline Operations
    :
    Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines from enterprise sources (ERP, SIS, CRM, HR, finance) to data lakehouse and downstream applications. Strong SQL/Python for data validation and investigation. Familiarity with lakehouse concepts and schema management.
  • Data Observability and Pipeline Monitoring
    :
    Experience with data observability platforms or equivalent monitoring tools tracking data freshness, volume, quality, and schema changes. Proficiency in designing alerting frameworks with meaningful signals and minimal noise.
  • Incident Management
    :
    Strong experience in troubleshooting and resolving AI system and data infrastructure issues, with ability to prioritize by business impact.
  • Performance Optimization
    :
    Techniques for resource allocation, scaling, and tuning of AI systems and data pipelines.
  • Change Management
    :
    Experience implementing changes to production AI systems and pipelines with testing, validation, and rollback procedures.
  • Data Quality Management
    :
    Understanding of data quality principles and remediation of issues such as missing records, nulls, duplicates, schema drift, and late-arriving data. Detect data quality failures before affecting downstream consumers.
  • Documentation and Knowledge Management
    :
    Excellence in creating and maintaining operational documentation…
Position Requirements
10+ Years work experience
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