Data Engineer (Snowflake) | Jobs in US

DigitalXNode; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

DigitalXNode is seeking a senior Data Engineer with deep Snowflake expertise to design and own scalable data pipelines and enterprise data platforms. You will work across Snowflake SQL components, RBAC, and performance tuning while collaborating with technical and business teams to deliver reliable data solutions.

The role requires strong SQL, Python, and ETL/ELT skills, plus experience with Azure or AWS and orchestration tools like Airflow or Azure Data Factory.

Qualifications

• 7+ years of professional experience in Data Engineering or a related field.
• 5+ years of practical experience with the Snowflake platform, encompassing Snowflake SQL, Tasks, Streams, Warehouse management, RBAC, clustering techniques, and performance tuning.
• Strong expertise in SQL, modern ETL/ELT frameworks, and data pipeline development.
• Experience building and maintaining enterprise Data Lakes and/or Data Warehouses.
• Experience working with cloud platforms such as Microsoft Azure or Amazon Web Services (AWS).
• Proficiency in Python for data engineering.
• Familiarity with Azure Data Factory and Apache Airflow to support scalable data processing.
• Strong understanding of data modeling concepts and distributed data systems.
• Excellent communication and collaboration skills across cross-functional teams.
• Strong problem-solving abilities and critical thinking.

Responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT processes on Snowflake.
• Develop and optimize Snowflake objects, including Snowflake SQL, Tasks, Streams, Warehouses, RBAC configurations, and clustering strategies.
• Build and maintain enterprise Data Lakes and/or Data Warehouses to support reporting and analytics.
• Work with cloud platforms such as Azure or AWS to support data ingestion, storage, and processing.
• Write production-grade Python code to support data engineering workflows.
• Use orchestration tools such as Azure Data Factory and/or Airflow to schedule and manage data pipelines.
• Apply data modeling and warehousing principles across distributed data systems.
• Own end-to-end solutions from requirements gathering through deployment with minimal oversight.
• Translate evolving business requirements into clear, structured technical solutions.
• Collaborate across technical and business teams and mentor junior engineers as needed.

Skills

Snowflake

SQL

Python

ETL/ELT

Data Warehouses

Data Lakes

Azure

AWS

Airflow

Data Modeling

Cloud Platforms

Communication

Education

Bachelor's degree in Computer Science, Engineering, Information Systems

Tools

Azure Data Factory

Apache Airflow

Databricks

Spark

Delta Lake

Job description

Company Overview

We are a talent acquisition and staff augmentation firm, and we are currently hiring on behalf of one of our clients, an organization in the retail and consumer technology space. Our client is looking to onboard a Data Engineer across multiple locations in the US. This is a remote role and is offered as contractual job.

Job Summary

We are looking for an experienced Data Engineer with deep, hands-on Snowflake expertise to design, build, and own scalable data pipelines and enterprise data platforms. In this role, you will work across Snowflake SQL, Tasks, Streams, Warehouses, RBAC, and performance optimization, while collaborating closely with technical and business teams to turn evolving requirements into structured, reliable data solutions.

Key Responsibilities

• Design, build, and maintain scalable data pipelines and ETL/ELT processes on Snowflake.
• Develop and optimize Snowflake objects, including Snowflake SQL, Tasks, Streams, Warehouses, RBAC configurations, and clustering strategies.
• Build and maintain enterprise Data Lakes and/or Data Warehouses to support business reporting and analytics.
• Work with cloud platforms such as Azure or AWS to support data ingestion, storage, and processing.
• Write efficient, production-grade Python code to support data engineering workflows.
• Use orchestration tools such as Azure Data Factory and/or Airflow to schedule and manage data pipelines.
• Apply strong data modeling and data warehousing principles across distributed data systems.
• Own end-to-end solutions, from requirements gathering through deployment, with minimal oversight.
• Translate ambiguous or evolving business requirements into clear, structured technical solutions.
• Collaborate across technical and business teams, and mentor junior engineers as needed.

Required Skills

• 7+ years of professional experience in Data Engineering or a related field.
• Demonstrated 5+ years of practical experience with the Snowflake platform, encompassing Snowflake SQL, Tasks, Streams, Warehouse management, RBAC, clustering techniques, and performance tuning.
• Strong expertise in SQL, modern ETL/ELT frameworks, and data pipeline development.
• Experience building and maintaining enterprise Data Lakes and/or Data Warehouses.
• Experience working with cloud platforms such as Microsoft Azure or Amazon Web Services (AWS), with the ability to develop, deploy, and support scalable, cloud-native applications.
• Proficiency in Python for data engineering.
• Familiarity with modern workflow orchestration frameworks, including Azure Data Factory and Apache Airflow, to support scalable data processing and pipeline management
• Strong understanding of data modeling concepts, data warehousing architectures, and distributed data systems, with the ability to design scalable and efficient data solutions.
• Excellent communication and collaboration skills, with proven ability to work effectively across cross-functional teams, bridging technical and business stakeholders to achieve project objectives.
• Strong problem-solving abilities and critical thinking.

AI Experience

• Comfort using AI-assisted tools to speed up query writing, pipeline debugging, or documentation is a plus.
• Awareness of how AI/automation is being introduced into data engineering workflows, though hands-on AI/ML experience is not required for this role.

Managerial Experience

• Collaborative mindset with the ability to mentor junior engineers.
• Proven ability to work independently and own solutions end to end, from requirement gathering through deployment.

Operational Experience

• Background working with enterprise data platforms handling high volumes and strict SLAs is a plus.
• Experience implementing data governance, metadata management, and data quality frameworks is a plus.
• Strong sense of ownership, accountability, and quality across delivered work.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent hands-on industry experience.
• Over 7 years of experience in Data Engineering, including at least 5 years of extensive hands-on expertise with Snowflake.

Certifications

• SnowPro certification(s) are a plus, though not mandatory.

Good to Have

• Proven experience designing and implementing CI/CD pipelines to support data engineering workflows and deployments.
• Knowledge of Delta Lake, Databricks, Spark, or other big data processing frameworks.