Job Summary
We are seeking an experienced Data Engineer with strong expertise in Snowflake, DBT (Data Build Tool), SQL, and Cloud platforms (AWS/Azure/GCP). The ideal candidate will design, build, and optimize scalable data pipelines, ensure data integrity, and enable advanced analytics and business intelligence solutions.
Key Responsibilities
Design, develop, and maintain Snowflake data warehouses, ensuring optimal performance, scalability, and cost efficiency.
Develop and manage data transformation pipelines using DBT (Data Build Tool) for efficient ELT/ETL processes.
Write complex SQL queries, stored procedures, and optimize database performance.
Implement cloud-based data solutions (AWS/Azure/GCP) including data lakes, storage, and compute services.
Collaborate with data analysts, scientists, and business teams to understand data requirements and deliver robust solutions.
Ensure data governance, security, and compliance with industry best practices.
Automate data workflows using orchestration tools like Airflow, Dagster, or similar.
Monitor, troubleshoot, and optimize data pipelines for reliability and efficiency.
Mentor junior engineers and contribute to best practices in data engineering.
Required Skills & Qualifications
7+ years of hands-on experience in data engineering.
Strong expertise in Snowflake (architecture, performance tuning, security).
Proficiency in DBT (Data Build Tool) for data transformation and modeling.
Advanced SQL skills (query optimization, complex joins, window functions).
Experience with any cloud platform (AWS, Azure, or GCP) and related services (S3, Redshift, BigQuery, etc.).
Knowledge of Python or another scripting language for automation.
Familiarity with data pipeline orchestration (Airflow, Prefect, Luigi).
Understanding of data warehousing concepts, dimensional modeling, and ETL/ELT best practices.
Experience with version control (Git) and CI/CD pipelines.
Strong problem-solving and analytical skills.
Preferred Skills
Snowflake certifications (SnowPro Core, SnowPro Advanced).
Experience with real-time data processing (Kafka, Spark Streaming).
Knowledge of data observability and monitoring tools (Great Expectations, Monte Carlo).