Intern - Data Engineer

Rysun Labs (formerly KCS – Krish Compusoft Services) is an AI, Data & Digital innovation partner of choice for enterprises. Rysun guides and accelerates the AI & Data strategy and Digital Transformation programs for Fortune 2000 enterprises and product startups to shape remarkable customer experiences and intelligent operations. The team delivers innovative, specialized solutions that help High-tech, Retail & Ecommerce, and Energy companies to outperform competition and lead the change in their industry.

Rysun partners with Microsoft, Google and AWS to bring the best of enterprise technology to its customers. Rysun believes in quality-first and is CMMI Level 5, ISO 9001 & 27001 certified. The team has a growth mindset fueled by a strong culture of collaboration that unifies its global teams across US, UK, India & South Africa (a proud Level 2 B-BBEE Contributor).

We are seeking a motivated and detail-oriented Data Engineering Intern to join our growing data team. This internship offers hands‑on experience in building and maintaining modern data pipelines, supporting AI/ML initiatives, and working with large‑scale data platforms. The ideal candidate is passionate about data, analytics, and emerging AI technologies and is eager to learn in a collaborative environment.

• Data Pipeline Development:
  Design, build, and maintain scalable ETL/ELT pipelines to ingest structured, semi-structured, and unstructured data into our modern data lakehouse architecture.
• AI-Ready Data Foundations:
  Cleanse, structure, and prepare data pipelines specifically optimized for downstream AI/ML use cases, including feature engineering and processing text for vector embeddings.
• Data Modeling:
  Assist in developing and maintaining robust enterprise data models (dimensional modeling, Medallion architecture—Bronze/Silver/Gold layers) ensuring data quality and integrity.
• Performance Tuning:
  Monitor, profile, and optimize data pipeline execution times, SQL queries, and storage patterns to ensure highly performant and cost-effective data processing.
• Data Quality & Governance:
  Implement automated data quality checks, schema validation, and monitor for data drift to ensure the reliability of analytics and AI outputs.
• Collaboration:
  
  Work in an agile environment alongside senior data engineers, data scientists, and business stakeholders to translate business requirements into technical data solutions.