Data Operations Engineer Intern (Internship)

Abacus Insights

6d•Remote

About The Position

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.

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.

Nice To Haves

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.

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