Senior Data Integration Operations Engineerr

About The Opportunity

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.

Responsibilities

• Maintain, schedule, and troubleshoot data integration pipelines that extract from enterprise source systems (ERP, SIS, CRM, HR, finance) and load into data lakehouse and downstream operational applications.
• Manage integration jobs that feed both the data lakehouse and downstream point solutions used across the university.
• Implement scheduled maintenance activities with minimal disruption to dependent systems, and manage user access and permissions according to security policies.
• Analyze integration pipeline performance metrics, identify bottlenecks, long-running jobs, and resource contention, and implement tuning and optimization measures.
• Contribute to the evaluation and implementation of the university’s data observability platform, helping define the monitoring strategy, key metrics, SLA thresholds, and alerting rules that will govern pipeline health across the integration landscape.
• Create and maintain comprehensive operational documentation, including runbooks, standard operating procedures, and knowledge base articles.
• Identify opportunities to automate repetitive operational tasks, improve pipeline reliability, and reduce manual intervention.
• Develop and implement scripts and workflows to streamline routine integration operations.
• Contribute to the ongoing evaluation of integration tools (including Fivetran) and the evolution of the university’s data integration practices based on operational experience and emerging best practices.

Qualifications

• Data Integration Platform Experience: Hands-on experience administering and operating enterprise data integration platforms, with Informatica PowerCenter or IDMC (Intelligent Data Management Cloud) strongly preferred. Experience with SaaS-based ELT tools such as Fivetran is a plus.
• Data Pipeline Operations: Extensive experience maintaining, scheduling, and troubleshooting data integration pipelines that extract from enterprise source systems (ERP, SIS, CRM, HR, finance) and load into data lakehouse and downstream operational applications. Strong SQL/Python skills are required for data validation, troubleshooting, and ad hoc investigation of pipeline issues. Familiarity with lakehouse architecture concepts (medallion architecture, incremental loads, schema management) is expected.
• Data Observability and Pipeline Monitoring: Experience with data observability platforms (such as Monte Carlo, Acceldata, Anomalo, or Datafold) or equivalent pipeline monitoring tools that track data freshness, volume, quality, and schema changes strongly preferred. Proficiency in designing alerting frameworks that surface meaningful signals without generating excessive noise.
• Incident Management: Strong experience in troubleshooting, diagnosing, and resolving AI system and data infrastructure issues, with the ability to prioritize incidents based on business impact.
• Performance Optimization: Knowledge of techniques to optimize AI system and data pipeline performance, including resource allocation, scaling strategies, and performance tuning.
• Change Management: Experience implementing changes to production AI systems and data pipelines with minimal disruption, including testing, validation, and rollback procedures.
• Data Quality Management: Strong understanding of data quality principles as they apply to integration pipelines, including detection and remediation of issues such as missing records, null rates, duplicate data, schema drift, and late-arriving data. Ability to identify data quality failures before they affect downstream analytics consumers or operational applications.
• Automation Skills: Ability to create and implement automation scripts and workflows to streamline routine operational tasks for both AI systems and data flows, enhancing overall system reliability.
• DevOps Practices: Familiarity with DevOps and CI/CD principles as applied to AI systems and data pipelines, including containerization, orchestration, and infrastructure as code.
• Security Awareness: Understanding of security best practices for AI operations and data handling, including access control, data protection, and vulnerability management.
• Collaboration Skills: Strong ability to work with cross-functional teams, communicate technical concepts clearly, and coordinate incident response activities effectively.
• Problem-solving: Excellent analytical and problem-solving skills, with the ability to troubleshoot complex issues in AI systems and data infrastructure in a methodical and efficient manner.
• Compliance Knowledge: Understanding of relevant regulations and compliance requirements affecting AI systems and data processing in higher education environments.
• Communication Skills: Clear and concise communication abilities, both written and verbal, to document procedures, report incidents, and coordinate with stakeholders.
• Service Management: Knowledge of IT service management principles and frameworks, with experience applying them to AI and data pipeline operations.