Intern, Data Engineering in Plantation, Florida at Pediatric Associates

PRIMARY FUNCTION

The Data Engineering Intern – Data Quality & Automation will support the Data Engineering and Quality Assurance teams in developing, validating, and maintaining reliable enterprise data solutions. Working under the guidance of senior team members, the intern will gain hands-on experience with data engineering, data quality, test automation, and quality engineering practices across data warehouses, data platforms, ETL/ELT pipelines, data migrations, reporting solutions, and enterprise applications.

This internship provides an opportunity to develop practical skills in SQL, Python, data engineering, data validation, ETL/ELT, data modeling, and automation while contributing to real-world enterprise data initiatives in a collaborative environment.

ESSENTIAL DUTIES AND RESPONSIBILITIES

1. Assist in developing, testing, and maintaining ETL/ELT pipelines, data transformations, and data integration workflows under the guidance of senior Data Engineers.
2. Write and optimize SQL queries to support data analysis, transformation, reconciliation, troubleshooting, and data validation activities.
3. Support data quality and validation activities across Data Warehouse, Data Lake, ETL/ELT pipelines, reports, dashboards, and enterprise applications.
4. Perform source-to-target data validation and reconciliation to ensure data accuracy, completeness, consistency, and integrity across systems.
5. Assist in developing automated data validation routines and test scripts for data pipelines, transformations, business rules, and data migration initiatives.
6. Support functional, integration, regression, API, and data testing activities as required.
7. Assist with data profiling and analysis to identify data anomalies, missing data, duplicates, transformation issues, and other data quality concerns.
8. Assist in validating data transformations, aggregations, calculations, business rules, and source-to-target mappings.
9. Gain exposure to data modeling concepts, including relational, dimensional, star schema, and data warehouse modeling techniques.
10. Assist in monitoring and troubleshooting data pipelines and participate in identifying the root causes of data quality and pipeline issues.
11. Develop and maintain test cases, test data, validation scripts, technical documentation, and data quality documentation.
12. Log, track, validate, and assist in resolving data and application defects using appropriate defect management tools.
13. Collaborate with Data Engineers, QA Engineers, Business Analysts, Data Analysts, Developers, and Product teams to understand business requirements and translate them into appropriate data and validation requirements.
14. Participate in Agile ceremonies, sprint planning, backlog refinement, technical discussions, code reviews, and QA review sessions.
15. Gain exposure to CI/CD, version control, and DevOps practices used for deploying and testing data engineering solutions.
16. Contribute to continuous improvement initiatives by identifying opportunities to improve data pipelines, data quality controls, testing processes, and automation.

SUPERVISORY RESPONSIBILITIES

None

QUALIFICATIONS

EDUCATION:

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Information Systems, Software Engineering, Data Analytics, or a related field.

LICENSURE:

None

EXPERIENCE

None

KNOWLEDGE, SKILLS AND ABILITIES

1. Basic understanding of relational databases, SQL, and database concepts.
2. Exposure to Python or another modern programming/scripting language.
3. Basic understanding of ETL/ELT, data integration, data warehousing, and data pipeline concepts.
4. Familiarity with software testing, data validation, and Software Development Lifecycle (SDLC) concepts.
5. Basic understanding of data quality principles, including accuracy, completeness, consistency, integrity, and reconciliation.
6. Exposure to cloud data platforms such as Azure, AWS, or GCP is a plus.
7. Exposure to data engineering technologies such as Databricks, Azure Data Factory, Snowflake, Azure Synapse, Apache Spark, or similar platforms is a plus.
8. Familiarity with APIs and API testing tools such as Postman is desirable.
9. Exposure to automation/testing frameworks such as PyTest, Selenium, or Playwright is a plus.
10. Familiarity with Git, Azure DevOps, Jira, or similar development and collaboration tools is desirable but not required.
11. Strong analytical, problem-solving, and troubleshooting skills.
12. Good communication, documentation, and teamwork abilities.
13. Strong interest in learning data engineering, data quality, automation, and cloud data technologies.

TYPICAL WORKING CONDITIONS

• Non-Patient Facing
• US Based
• Remote