Snowflake Data Engineer

Kryon Knowledge Works Group; United States; Remote; USD 110,000 - 160,000; Full time

Job summary

Kryon Knowledge Works Group is seeking an experienced Snowflake Data Engineer in the United States to design and maintain scalable data pipelines.

The candidate will leverage Snowflake, advanced SQL, Python, and ETL to deliver robust data warehousing solutions, with Airflow orchestration on AWS.

This role emphasizes data quality, performance optimization, and collaboration across Agile teams to support business reporting and analytics.

Qualifications

• Hands-on Snowflake data engineering experience.
• Strong SQL expertise and performance tuning.
• Proficiency in Python for data processing.
• Experience building and maintaining ETL pipelines.
• Experience with Apache Airflow orchestration.
• Experience with AWS services, S3 and related connections.
• Knowledge of data warehousing architecture and schema design.
• Experience with dimensional modelling and SCD.
• Ability to analyze data and support business reporting.
• Experience with data validation and reconciliation.
• Familiarity with Agile methodologies and product life cycle.
• Experience with data solutions and software development practices.

Responsibilities

• Develop and maintain data warehouse solutions using Snowflake.
• Write complex SQL queries and optimise query performance.
• Develop Python-based data processing solutions.
• Build and maintain ETL pipelines using SQL and other ETL tools.
• Manage data pipeline orchestration using Apache Airflow.
• Work with AWS services, specifically Amazon S3 and AWS Connection Management.
• Design data warehouse schemas based on business requirements.
• Implement dimensional modelling using fact and dimension tables.
• Implement Slowly Changing Dimensions (SCD).
• Analyse data and support business reporting requirements.
• Reconcile business information and validate data accuracy.
• Troubleshoot data pipeline issues, perform root cause analysis, and implement solutions.
• Follow Agile methodologies and collaborate with teams throughout the product life cycle.
• Apply software development practices and adopt current technologies for data solutions.

Skills

Snowflake data warehousing

Advanced SQL

Python

ETL development

Apache Airflow

AWS S3 and connections

Data warehousing architecture

Dimensional modelling

Slowly Changing Dimensions

Data analysis and reporting

Data reconciliation and validation

Agile methodologies

Product life cycle

Troubleshooting and root cause

Software development practices

Technology adoption

Job description

Job Summary

We are looking for an experienced Snowflake Data Engineer with hands-on experience in Snowflake, advanced SQL, Python, ETL development, Apache Airflow, and AWS. The candidate should have strong knowledge of data warehousing, data modelling, data validation, and data pipeline troubleshooting.

Key Responsibilities

• Develop and maintain data warehouse solutions using Snowflake.
• Write complex SQL queries and optimise query performance.
• Develop Python-based data processing solutions.
• Build and maintain ETL pipelines using SQL and other ETL tools.
• Manage data pipeline orchestration and scheduling using Apache Airflow.
• Work with AWS services, specifically Amazon S3 and AWS Connection Management.
• Design data warehouse schemas based on business requirements.
• Implement dimensional modelling using fact and dimension tables.
• Implement Slowly Changing Dimensions (SCD) concepts.
• Analyse data and support business reporting requirements.
• Reconcile business information and validate data accuracy.
• Troubleshoot data pipeline issues, perform root cause analysis, and implement solutions.
• Follow Agile methodologies and collaborate with teams throughout the product life cycle.
• Apply software development practices and adopt current technologies for data solutions.

Required Skills

• Snowflake data warehousing and data engineering
• Advanced SQL and query performance optimisation
• Python
• ETL development
• Apache Airflow
• AWS, Amazon S3, and AWS Connection Management
• Data warehouse architecture and schema design
• Dimensional modelling (fact and dimension tables)
• Slowly Changing Dimensions (SCD)
• Data analysis and business reporting
• Business information reconciliation and data validation
• Agile methodology
• Product life cycle
• Troubleshooting and root cause analysis
• Software development practices
• Technology adoption