Data Engineer

As a Data Engineer, you will work on building, testing, and maintaining data pipelines and connectors that integrate data from various sources (APIs, databases, files, SaaS applications) into the data platform. You will be trained and mentored to work on scalable, reliable, and high-quality data ingestion systems.

This role is ideal for candidates who want to build a strong foundation in Data Engineering, ETL, APIs, and large-scale data systems.

Key Responsibilities:

• Assist in developing and maintaining data connectors and ingestion pipelines for different data sources (APIs, databases, files, SaaS tools).
• Work on extracting, transforming, and loading (ETL/ELT) data into the data platform.
• Understand source system schemas, APIs, and data formats (JSON, CSV, relational tables).
• Implement basic data transformations, validations, and checks to ensure data quality and reliability.
• Write clean, maintainable, and testable code under guidance from senior engineers.
• Perform unit testing and basic integration testing of data pipelines and connectors.
• Help in debugging data issues, pipeline failures, and sync issues.
• Contribute to documentation of connectors, pipelines, and operational runbooks.
• Use Git and CI/CD pipelines for code versioning and deployments.
• Follow engineering best practices, coding standards, and data governance guidelines.
• Collaborate with senior data engineers, QA, and product teams.

Required Skills & Qualifications:

• Bachelor's degree in computer science, IT, Engineering, or related field.
• Basic to good knowledge of Python and SQL.
• Basic understanding of Data Engineering concepts: ETL, data pipelines, data warehousing.
• Familiarity with REST APIs and working with JSON / CSV data.
• Basic understanding of databases (MySQL, Postgres, or similar).
• Awareness of Git / version control systems.
• Willingness to learn debugging, monitoring, and production support for data pipelines.
• Good problem-solving attitude and attention to detail.
• Good communication skills and eagerness to learn.

Good to Have (Not Mandatory):

• Exposure to PySpark / Spark / Databricks / EMR.
• Basic understanding of cloud platforms (AWS/Azure/GCP).
• Knowledge of CI/CD concepts.
• Internship or academic project in Data Engineering / Backend / ETL.
• Basic understanding of data modelling and data warehousing.

Skills

Data Warehousing, ELT, ETL, Python, SQL