Data Operations Engineer Intern, Abacus Insights

• Strong interest in data engineering, data operations, and production data systems
• Currently pursuing or recently completed a Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related quantitative discipline
• Solid understanding of ETL/ELT architectures, including ingestion, transformation, validation, orchestration, and error handling
• Proficiency in SQL, including complex joins, aggregations, window functions, and debugging data discrepancies at scale
• Working knowledge of Python for data processing, automation, and operational tooling
• Familiarity with workflow orchestration tools such as Apache Airflow, including DAG design, scheduling, retries, and dependency management
• Experience or exposure to data integration platforms such as Airbyte, including connector-based ingestion, schema evolution, and sync monitoring
• Understanding of Master Data Management (MDM) concepts and tools, with exposure to platforms such as Rhapsody, Onyx, or other enterprise MDM solutions
• Knowledge of data pipeline observability, including log analysis, metrics, alerting, and debugging failed jobs
• Exposure to cloud platforms (preferably AWS), with familiarity in services such as S3, Lambda, EMR, EKS, or managed data processing services
• Ability to communicate technical issues clearly and concisely, including writing actionable bug reports and collaborating on incident resolution
• Strong documentation habits and attention to detail in operational workflows

Desirable

• We are seeking a Data Operations Engineer Intern to join our TechOps organization within the Connector Factory team
• This role provides hands-on experience supporting production grade data pipelines responsible for ingesting, transforming, validating, and delivering healthcare data from numerous external sources
• As an intern, you will work closely with senior data, platform, and operations engineers to monitor pipeline health, debug data and system issues, automate operational workflows, and improve data reliability
• You will gain exposure to both batch and streaming architectures and learn how modern data platforms are deployed and operated using AWS services such as Lambda, EMR, EKS, and Databricks
• Monitor production data pipelines and systems, identifying failures, latency issues, schema changes, and data quality anomalies
• Debug pipeline failures by analyzing logs, metrics, SQL outputs, and upstream/downstream dependencies
• Assist in root cause analysis (RCA) for data incidents and contribute to implementing corrective and preventive solutions
• Support the maintenance and optimization of ETL/ELT workflows to improve reliability, scalability, and performance
• Automate recurring data operations tasks using Python, shell scripting, or similar tools to reduce manual intervention
• Assist with data mapping, transformation, and normalization efforts, including alignment with Master Data Management (MDM) systems
• Collaborate on the generation and validation of synthetic test datasets for pipeline testing and data quality validation
• Shadow senior engineers to deploy, monitor, and troubleshoot data workflows on AWS, Databricks, and Kubernetes-based environments
• Ensure data integrity and consistency across multiple environments (development, staging, production)
• Clearly document bugs, data issues, and operational incidents in Jira and Confluence, including reproduction steps, impact analysis, and resolution details
• Communicate effectively with cross-functional, onsite, and offshore teams to escalate issues, provide status updates, and track resolutions
• Participate in Agile ceremonies and follow structured incident and change management processes