Description

Key Responsibilities:

Design, develop, and maintain scalable ETL / ELT pipelines

Build and optimize data architectures and data models

Develop reliable and efficient data workflows for structured and unstructured datasets

Work with cloud platforms such as AWS, Azure, or GCP

Develop and manage data warehouse solutions (Snowflake, BigQuery, Redshift, etc.)

Collaborate with analytics, engineering, and business teams for data requirements

Ensure data quality, integrity, and governance standards

Optimize SQL queries and data processing performance

Monitor pipeline reliability and troubleshoot production issues

Work with orchestration and workflow tools like Airflow, dbt, or equivalent

Required Skills:

Strong hands-on experience in Python and SQL

Experience with ETL / ELT processes and data pipeline development

Cloud experience (AWS / Azure / GCP)

Knowledge of Snowflake / BigQuery / Redshift

Experience with Airflow, dbt, or similar orchestration tools

Strong understanding of data warehousing concepts

Good communication and stakeholder management skills

Strong analytical and problem-solving abilities

Ability to work effectively within cross-functional teams

Good to Have:

Experience in data modeling and performance optimization

Exposure to streaming / real-time data processing

Knowledge of data governance and data quality frameworks

Experience working with large-scale distributed data systems

Preferred Candidate Profile:

3–6 years of relevant Data Engineering experience

Bachelor’s / Master’s degree in Computer Science, IT, Engineering, or related field

Key Skills

Education

Bachelor's or Master's degrees