Data Engineer

This role focuses on designing, developing, and optimizing scalable data transformation pipelines within modern cloud data platforms. You will be responsible for building analytics-ready datasets and implementing robust data quality frameworks to support enterprise-level decision-making. The position requires a strong technical background in ELT processes and a commitment to delivering high-quality, reliable data products.

We are partnering with a leading global computer software firm that specializes in delivering innovative digital solutions and data-driven insights. With a strong focus on technological excellence, the company supports large-scale enterprise transformations across various industries. They foster a collaborative, fast-paced environment that encourages professional growth and technical innovation.

Responsibilities

• Design and develop efficient ELT pipelines using DBT to support complex data workflows.
• Build reusable, scalable, and maintainable data transformation models following industry best practices.
• Develop staging, intermediate, and mart layers to ensure clean and accessible data structures.
• Design dimensional models, fact tables, and star schemas to support enterprise data warehouse initiatives.
• Implement comprehensive DBT tests for uniqueness, referential integrity, and custom business rules.
• Optimize query performance and manage compute costs across cloud data platforms.
• Integrate DBT projects with Git and CI/CD pipelines to automate deployments across environments.
• Collaborate with data architects, analysts, and business stakeholders to deliver high-impact data solutions.

Requirements

• Proven experience as a Data Engineer with deep expertise in DBT (Data Build Tool).
• Strong proficiency in SQL and experience with ELT processes.
• Hands-on experience with cloud data warehousing platforms such as Snowflake, Databricks, BigQuery, Azure Synapse, or Redshift.
• Solid understanding of data modeling techniques, including star schemas and dimensional modeling.
• Experience with CI/CD practices, Git version control, and automated deployment workflows.
• Strong analytical skills with the ability to perform root cause analysis for data quality issues.
• Excellent communication skills and the ability to work effectively in a hybrid team environment.