Sr. Data Engineer - Snowflake and Lakehouse

About the role

As a Senior Data Engineer - Snowflake and Lakehouse, you will make an impact by designing, building, and optimizing scalable data solutions that power analytics, reporting, and data-driven decision-making across the enterprise. You will be a valued member of the data engineering team and work collaboratively with client technology leads, analytics teams, data scientists, and platform engineers to deliver modern Lakehouse capabilities using Snowflake and the modern data stack.

We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.

Responsibilities

• Design and build scalable batch and near real-time data ingestion pipelines using Snowpipe and Snowpipe Streaming.
• Develop and maintain robust data transformation frameworks and curated data models using dbt and industry best practices.
• Build and optimize data processing solutions using Snowpark (Python and Spark) to support advanced transformations and machine learning use cases.
• Implement and manage Dynamic Tables, Snowflake Tasks, and workflow orchestration to automate and streamline data operations.
• Design and maintain Apache Iceberg-based Lakehouse architectures that enable open, interoperable, and high-performing data platforms.
• Integrate Kafka event streams and CDC data sources into Snowflake to support scalable real-time analytics.
• Monitor data platform health, optimize warehouse utilization, and implement cost and performance controls.
• Establish data quality standards, schema validation processes, testing frameworks, and operational best practices.
• Partner with cross-functional stakeholders to define data contracts, governance standards, and Lakehouse architecture guidelines.
• Create and maintain technical documentation, architecture diagrams, data lineage artifacts, and operational runbooks.

Requirements

• Minimum 5 years of hands-on experience delivering data engineering solutions with Snowflake in enterprise environments.
• Strong experience with Snowflake platform capabilities including virtual warehouses, security roles, tasks, performance tuning, and operational management.
• Proven experience implementing batch and real-time ingestion solutions using Snowpipe and Snowpipe Streaming.
• Hands-on experience with dbt for data transformations, testing, documentation, and version-controlled development practices.
• Strong experience with Snowpark using Python and Spark for scalable data processing and transformation workloads.
• Experience integrating Kafka event streams and/or Change Data Capture (CDC) pipelines with Snowflake.
• Hands-on experience with AWS services such as S3, IAM, Glue, and Lambda supporting cloud-based data platforms.
• Advanced SQL skills and strong understanding of data modeling, dimensional design, and Lakehouse architecture principles.
• Experience with