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Enterprise Data Warehouse (EDW) Engineer

February 6, 2026
Experience: 4-15 years Work from Office (Monday to Friday) About the Role We are seeking a Mid-Level Enterprise Data Warehouse (EDW) Engineer with a passion for building scalable, cloud-native data solutions. This position is ideal for individuals who excel in collaborative environments, communicate effectively, and approach challenges with a problem-solving mindsetnot just those who follow instructions. Key Responsibilities
  • Design, develop, and maintain high-performance data pipelines and ETL/ELT workflows for the enterprise data warehouse.
  • Work with cloud-based data warehouse platforms like Snowflake, BigQuery, or Redshift to optimize data storage and retrieval.
  • Write clean, efficient, and maintainable SQL and Python code for data transformation and automation tasks and Implement and manage CI/CD pipelines for data workflows using tools like Git, Jenkins, or GitHub Actions.
  • Leverage orchestration tools (e.g., Apache Airflow, dbt Cloud, Prefect) to schedule and monitor data workflows.
  • Conduct detailed data analysis between current and target systems, and prepare mapping documentation.
  • Collaborate with data analysts, scientists, and business teams to generate actionable insights.
  • Proactively identify and address data quality issues and performance bottlenecks.
  • Contribute to data architecture decisions and establish best practices.
Required Qualifications
  • 4-15 years of experience in Data Engineering or EDW Development.
  • Strong hands-on experience with Snowflake, BigQuery, or Redshift.
  • Expertise in SQL and Python.
  • Working knowledge of CI/CD tools such as Git, Jenkins, and GitHub Actions.
  • Experience with workflow orchestration tools like Airflow and Prefect.
  • Ability to analyze large datasets and present findings in a business context.
  • Excellent communication and teamwork skills.
  • A proactive, solution-oriented mindset with strong ownership and accountability.
Preferred Qualifications
  • Experience in data modeling and architecture.
  • Understanding data governance, security, and compliance best practices.
  • Familiarity with modern data stack tools such as dbt, Fivetran, or Looker
  • Experience with large-scale enterprise data warehouse implementations.