Data Engineer Trainee

Company Description
Saasta Software & Services is a technology-driven IT startup dedicated to powering digital innovation through scalable and reliable solutions for businesses of all sizes. The company focuses on simplifying complex technologies so organizations can adopt secure and modern digital systems with confidence. Saasta emphasizes building solutions that are both robust and adaptable to a rapidly evolving digital landscape. Team members contribute to products and services designed to help clients achieve their goals efficiently and securely. The work environment values innovation, learning, and practical problem-solving.
About The Role
Saasta Software & Services is looking for a motivated and technically curious Data Engineer Trainee to join our engineering team. The candidate will work on designing, developing, testing, and maintaining data pipelines, ETL/ELT workflows, databases, and cloud-based data solutions.
This position is ideal for fresh graduates or professionals with up to 1 year of experience who want to build a career in Data Engineering, Cloud Computing, Analytics, and AI.
Key Responsibilities
Develop and maintain ETL/ELT data pipelines for structured and semi-structured data.
Extract, transform, validate, and load data from multiple sources.
Work with relational and NoSQL databases.
Write optimized SQL queries, stored procedures, and data transformation logic.
Assist in designing and maintaining data models, schemas, and data warehouses.
Perform data cleansing, validation, deduplication, and quality checks.
Develop Python-based scripts for data processing and automation.
Work with APIs, files, databases, and other data sources for ingestion.
Monitor data pipelines and troubleshoot failures and data-quality issues.
Assist in implementing data solutions on AWS or other cloud platforms.
Collaborate with Data Scientists, AI Engineers, Software Engineers, QA Engineers, and Business Analysts.
Follow coding standards, documentation practices, version control, and security best practices.
Participate in code reviews, testing, deployment, and production support.
Learn and work with modern Data Engineering, AI, and cloud technologies.
Required Skills & Qualifications
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
0 to 1 year of experience in E2E Data Engineering and its process
Good understanding of SQL and relational databases.
Basic to intermediate knowledge of Python.
Understanding of ETL/ELT concepts and data pipelines.
Knowledge of databases such as:
PostgreSQL / MySQL / SQL Server / MongoDB
Understanding of data structures and basic algorithms.
Familiarity with Git/GitHub/GitLab.
Basic knowledge of REST APIs and JSON.
Understanding of data cleaning and transformation techniques.
Cloud & Data Technologies
Knowledge of any of the following will be an advantage:
AWS
Azure
Google Cloud
Amazon S3
AWS Lambda
AWS Glue
Amazon RDS
Amazon Redshift
Apache Spark
Apache Airflow
Databricks
Kafka
Candidates are not required to know all technologies. Strong fundamentals and willingness to learn are important.
Key Competencies
Strong analytical and problem-solving ability.
Good communication and collaboration skills.
Attention to detail and data accuracy.
Ability to learn new technologies quickly.
Logical thinking and debugging skills.
Ownership and accountability.
Ability to work effectively in a startup environment.
Strong interest in Data Engineering, Cloud, AI, and emerging technologies.
Career Growth
Data Engineer Trainee → Junior Data Engineer → Data Engineer → Senior Data Engineer → Lead Data Engineer / Data Architect
Why Join Saasta Software & Services?
Work on real-world technology and AI projects.
Exposure to modern cloud and data engineering technologies.
Opportunity to work with cross-functional engineering teams.
Learn and contribute in a fast-growing IT startup environment.
Opportunity to develop expertise across Data Engineering, Cloud, AI, and Generative AI.
Build a strong technical foundation and long-term engineering career.
Location
Remote / Hybrid / Onsite

Work arrangement
No