Data Engineer

We are looking for a highly experienced Data Engineer who can help design, build, and optimize scalable data solutions using Snowflake, AWS, Python, and PySpark. This role is ideal for someone who enjoys solving complex data challenges, building modern data pipelines, and working in a fast-paced, collaborative environment. What You’ll Do • Design and develop robust end-to-end data pipelines and integration solutions using Snowflake, AWS services, and PySpark. • Build new ingestion and data processing frameworks leveraging Snowflake capabilities, Python, and cloud-native tools. • Work hands-on with Snowflake architecture, including Virtual Warehouses, Storage/Caching, Snowpipe, Streams, Tasks, and Stages. • Write optimized SQL, stored procedures (JavaScript or Python-based), and scripts to support data transformations and automation. • Collaborate across teams to implement data warehousing best practices (ETL/ELT, dimensional modeling, star/snowflake schema). • Use orchestration tools such as Airflow, dbt, Azure Data Factory, or similar to schedule and manage workflows. • Apply Git-based version control and CI/CD best practices to ensure high-quality, maintainable code. • Work within an Agile environment, communicating clearly with team members and stakeholders. What You Bring • 10+ years of experience in data engineering or data integration roles. • Expert-level skills in Snowflake along with deep experience integrating Snowflake with AWS services and PySpark. • 8+ years of hands-on experience with core data engineering tools and technologies: • Snowflake ecosystem • AWS (S3, Lambda, IAM, Glue, etc.) • Core SQL • Python Programming • 5+ years building new data pipeline frameworks using AWS + Snowflake + Python. • Strong understanding of data warehousing concepts, including data modeling and schema design. • Proficiency in SQL query optimization and performance tuning. • Familiarity with Git, DevOps practices, and CI/CD pipelines. • Excellent communication, teamwork, and problem-solving skills.