Data Engineer

Scope of Work:

• SQL: Strong understanding of SQL queries, joins, aggregations, Common Table Expressions (CTEs), window functions, and query optimization.
• Programming: Proficiency in Python for data processing, automation, and pipeline development.
• Databases: Hands-on experience with relational databases such as PostgreSQL or MySQL.
• Data Warehousing: Familiarity with analytical databases such as Google BigQuery
• ETL/ELT Development: Ability to build and maintain data ingestion, transformation, and loading processes.
• Workflow Orchestration: Familiarity with Apache Airflow, Mage.ai, or similar workflow orchestration tools.
• Data Transformation: Understanding of data transformation frameworks such as dbt or equivalent SQL-based transformation approaches.
• Data Modeling: Knowledge of dimensional modeling, fact and dimension tables, and star schema design.
• Cloud Technologies: Basic knowledge of cloud storage, compute, and managed data services.
• Version Control: Experience using Git for source code management and collaboration.
• Data Quality: Ability to implement basic data validation, logging, and pipeline monitoring.
• Problem-Solving: Strong analytical thinking, debugging skills, and ability to investigate data inconsistencies.
• Collaboration: Ability to communicate technical concepts clearly and work effectively with analysts, engineers, and business stakeholders.

Requirements:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
• 1–3 years of experience in Data Engineering, Data Analytics, Software Engineering, or a related technical role.
• Experience developing, maintaining, and optimizing ETL/ELT data pipelines.
• Experience working with relational databases, data warehouses, and large datasets.
• Understanding of data modeling, data transformation, and data integration concepts.
• Familiarity with batch processing and basic data orchestration workflows.
• Experience integrating data from multiple sources, including databases, APIs, and files.
• Understanding of data quality, consistency, validation, and monitoring practices.
• Familiarity with cloud-based data platforms such as Google Cloud Platform (GCP), AWS, or Azure.
• Ability to troubleshoot data pipeline issues and collaborate with cross-functional teams.
• Willingness to learn new technologies and follow data engineering best practices.