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.