Data Engineer

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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

• 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.