Fresher-Junior Data Engineer (AI/ML Focus)

• Key Responsibilities

• Support the design, development, and maintenance of data pipelines and ETL/ELT processes for analytics and AI/ML use cases.
• Assist in building and optimizing data processing jobs using Spark (PySpark) on AWS Glue under guidance from senior engineers.
• Help implement data transformation logic using dbt to produce clean and reliable datasets for analytics and machine learning.
• Support development and maintenance of data workflows using AWS MWAA (Apache Airflow), including contributing to Airflow DAGs.
• Write and optimize SQL queries for data extraction and basic transformation using Amazon Athena or similar services.
• Assist in preparing data pipelines for AI/ML workflows such as training and inference.
• Work with structured and unstructured datasets to support analytics and ML tasks.
• Collaborate with data analysts, data scientists, and ML engineers to deliver data solutions.
• Support integration with ML platforms (e.g., AWS SageMaker or similar) for model development and deployment.
• Assist in monitoring and troubleshooting data pipelines to ensure reliability and data quality.
• Document data pipelines, workflows, and technical processes.

• 1-3 years of experience in Data Engineering, Software Engineering, or related fields.
• Basic understanding of data engineering concepts, ETL/ELT pipelines, and data warehousing.
• Hands-on experience with SQL (writing queries and basic optimization).
• Familiarity with Python for data processing.
• Exposure to Spark (PySpark preferred) is a strong advantage.
• Familiarity with AWS services such as S3, Glue, Athena, MWAA, or IAM is a plus.
• Basic understanding of Airflow or workflow orchestration concepts.
• Understanding of ML fundamentals (data preparation, training, deployment basics) is a plus.
• Experience working with datasets (structured or unstructured).
• Good analytical and problem-solving skills.
• Good communication skills and willingness to work in an international environment.

Preferred Qualifications (Nice to Have)

• Exposure to AI/ML projects (data preprocessing, feature engineering, model training basics).
• Familiarity with tools such as SageMaker, MLflow, Databricks, or similar platforms.
• Awareness of Generative AI concepts (LLM, RAG, vector databases) is a plus.
• Exposure to dbt or Airflow in academic/personal/work projects.
• Basic understanding of Infrastructure as Code (Terraform or similar).