Senior Data Engineer II

• Design, develop, and maintain scalable data pipelines using Python, Snowflake, AWS Glue, and Airflow.
• Build and optimize ETL/ELT workflows to ingest, transform, and deliver data from various sources.
• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability.
• Collaborate with analysts, scientists, and stakeholders to understand requirements and deliver solutions.
• Establish and enforce data engineering best practices, coding standards, and design patterns.
• Optimize query performance and data storage strategies in Snowflake and cloud data warehouses.
• Implement CI/CD pipelines for automated testing and deployment of data workflows.
• Monitor pipeline performance, troubleshoot issues, and implement improvements proactively.
• Create and maintain technical documentation for data architectures, pipelines, and processes.
• Participate in code reviews and provide constructive feedback to team members.
• Mentor junior data engineers on modern tools, techniques, and best practices.
• Stay current with emerging data technologies and evaluate their applicability to business needs.
• Collaborate with DevOps and platform teams to ensure infrastructure scalability and reliability.
• Support data governance initiatives and ensure compliance with security policies.

• 5+ years of experience in data engineering or related field.
• Bachelor's Degree in Computer Science, Engineering, or equivalent practical experience.
• Expert proficiency in Python for data engineering, ELT development, and automation.
• Strong SQL skills with experience in query optimization and performance tuning.
• Hands-on experience with Snowflake (SnowSQL, stored procedures, streams/tasks).
• Proficiency with AWS data services: Glue, S3, Lambda, Redshift.
• Experience with Apache Airflow or similar orchestration tools (Luigi, Prefect, Dagster).
• Knowledge of data modeling principles (dimensional modeling, data vault, normalization).
• Experience with streaming technologies (Kafka, Kinesis) and dbt for analytics engineering.
• Understanding of DataOps, CI/CD for data pipelines, and infrastructure as code (Terraform).
• Familiarity with version control (Git), code reviews, and collaborative development.
• Knowledge of data quality frameworks (Great Expectations) and containerization (Docker/Kubernetes) is a plus.
• Understanding of data governance, security best practices, and compliance requirements.
• Strong problem-solving skills and ability to research new technologies independently.
• Excellent communication skills for technical and non-technical audiences.

• Private Medical/Dental Plan
• Savings Fund
• Life Insurance
• Meal/Grocery Voucher

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