Data Engineer

Assist with the development and maintenance of the core data warehouse structure and help with the transition to a lakehouse.

Help evaluate existing Snowflake workloads and identify candidates for migration, optimization, or hybrid operation.

Identify performance issues and assist with solution plans.

Assist with the development and maintenance of dbt models across staging, intermediate, mart, and semantic layers, following established project structure, testing, and documentation standards.

Help build and maintain data pipelines that ingest data from source systems into Snowflake, collaborating with platform and analytics teams on requirements and priorities.

Write and optimize SQL transformations for performance, readability, and maintainability within Snowflake.

Contribute to data quality and reliability efforts, including schema validation, source freshness checks, row-level testing, and pipeline monitoring.

Participate in CI/CD processes for data, including automated testing, code review, and deployment practices using Git-based workflows.

Contribute to Snowflake and cloud infrastructure configuration, including warehouse sizing, access patterns, and integration with surrounding services, under the guidance of senior engineers.

Support orchestration workflows and data pipeline scheduling, helping ensure pipelines run reliably and recover gracefully from failures.

Participate in design reviews, offering input on implementation approaches and learning from senior engineers’ architectural decisions.

Troubleshoot and resolve pipeline and data quality issues, conduct root cause analysis and implement fixes.

Document work clearly, including data models, pipelines, and operational runbooks, so the broader team can understand and maintain what you build.

3+ years of experience in data engineering, analytics engineering, or a closely related technical field.

Hands-on experience with Snowflake, including writing performant SQL, understanding warehouse behavior, and working with Snowflake’s access and security model.

Understanding of dimensional modeling, data marts, data quality controls, and enterprise reporting needs.

Hands-on experience with dbt, including building and testing models, using macros, and following layered project structures.

Solid understanding of data modeling concepts, including dimensional modeling and common transformation patterns.

Proficiency in SQL for analytical and transformation workloads, including debugging and performance along with familiarity with common tools and practices used to do so.

Experience with Git-based version control and collaborative development workflows.

Familiarity with building or supporting data pipelines for analytics and/or AI/ML use cases.

Exposure to CI/CD practices for data, cloud infrastructure, and orchestration tooling.

Strong training plans and materials provided.

Competitive Medical, Dental & Vision Insurance.

Company-Paid Life Insurance & 401(k).

Generous PTO, Sick Time & Paid Holidays.

Hybrid Scheduling after probation period.

Inclusive, team-oriented culture where people come first.