We are looking for a Data Engineer to design, optimize, and maintain production-grade data pipelines across diverse cloud platforms. The ideal candidate will have strong SQL and Python skills, hands-on experience with modern data tools, and a passion for building scalable, high-quality data systems that empower analytics and AI initiatives.

Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines using tools like dbt, Airflow, or Azure Data Factory.
• Develop seamless integrations across Snowflake, Databricks, and cloud storage platforms (S3, ADLS, GCS).
• Build and maintain data models to support analytics, BI, and reporting teams.
• Implement data quality, lineage, and monitoring frameworks to ensure reliable data delivery.
• Optimize SQL queries, data warehouse costs, and pipeline performance.
• Collaborate with BI and AI teams to enhance data accessibility and consistency.
• Enforce best practices in version control, CI/CD, and data governance.

Requirements:

• Strong command of SQL and Python (including Pandas or PySpark).
• Hands-on experience with two or more cloud platforms (AWS, Azure, or GCP).
• Proficiency with Snowflake (Snowpipe, Streams, Tasks) or Databricks (Delta Lake, Spark).
• Familiarity with data orchestration tools (Airflow, dbt, or Dagster).
• Basic understanding of DevOps concepts (Docker, GitHub Actions).