Data Operations Engineer Intern (Internship)
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Requirements
• 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. • What we would like to see, but not required: • Experience with cloud data warehouses such as Snowflake or BigQuery. • Familiarity with Databricks, Apache Spark, or distributed data processing frameworks. • Hands-on experience building automation for data operations or reliability engineering. • Exposure to healthcare data standards, regulated data environments, or HIPAA-compliant systems. • Compensation: Compensation for this role is based on experience, skills, and location, and includes base salary plus eligibility for performance bonuses and equity grants.
Responsibilities
• 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
Benefits
• What you’ll get in return: • Unlimited paid time off – recharge when you need it • Work from anywhere – flexibility to fit your life • Comprehensive health coverage – multiple plan options to choose from • Equity for every employee – share in our success • Growth-focused environment – your development matters here • Monthly cell phone allowance – stay connected with ease #LI-RF1 #LI-Remote