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.
- 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 of 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.
