Assist in developing, testing, deploying, and monitoring batch and near-real-time data pipelines under the guidance of senior data engineers and architects.
Ingest, transform, and integrate structured and semi-structured data from databases, files, APIs, enterprise applications, and cloud platforms.
Write clear, efficient, and maintainable SQL and Python code for data extraction, transformation, validation, and automation.
Support the creation of reusable ETL/ELT components, data models, and curated datasets for analytics, reporting, and AI/ML use cases.
Implement data quality checks covering completeness, validity, consistency, uniqueness, and timeliness; investigate and help resolve data issues.
Prepare, clean, label, and version datasets required for machine-learning experimentation and model lifecycle activities.
Work with data scientists, analysts, application teams, and business stakeholders to understand data requirements and translate them into technical tasks.
Create and maintain technical documentation, source-to-target mappings, data dictionaries, lineage information, operating procedures, and support notes.
Use version control, code reviews, testing, and CI/CD practices to deliver reliable and traceable data engineering changes.
Assist with operational monitoring, incident analysis, root-cause identification, and continuous improvement of pipeline performance and reliability.
Follow enterprise requirements for data security, privacy, access control, retention, and responsible use of data and AI.
Continuously build knowledge of data engineering, cloud services, data architecture, analytics, and AI/ML technologies.