Data Engineer

Horizon Air Freight

About The Position

We are seeking a skilled and forward‑thinking Data Engineer to play a key role in building and optimizing our modern data ecosystem. In this role, you will design and maintain robust data pipelines, manage and enhance our Snowflake data warehouse, and ensure the accuracy, reliability, and scalability of the data that powers critical business insights. You will collaborate closely with analysts, data scientists, and cross‑functional partners to deliver high‑quality datasets that support reporting, analytics, and strategic decision‑making. This position is ideal for someone who thrives in a dynamic, evolving environment and is excited by the opportunity to shape data architecture from the ground up. If you enjoy solving complex data challenges, driving process improvements, and helping build a strong data foundation for the future, we'd love to meet you.

Nice To Haves

Experience with Snowflake or other cloud data warehouses.
Hands-on experience with Fivetran, dbt, Airflow, or similar ETL/ELT tools.
Strong SQL skills and familiarity with query optimization techniques.
Proficiency in Python or another programming language for data engineering.
Familiarity with data modeling concepts (Kimball, Data Vault, star/snowflake schemas).
Experience working in a collaborative, fast-paced environment.
Comfortable helping inform decisions about new pipeline architecture - and should not shy away from breaking new ground in our data maturation. This includes being comfortable with change and building something from nothing.

Responsibilities

Design, implement, and maintain automated data pipelines to ingest, transform, and deliver data across multiple systems.
Build and manage data connectors using tools such as Fivetran or custom integrations to ensure seamless data flow from source systems (e.g., CRM, ERP, application databases).
Maintain and optimize our Snowflake data warehouse to ensure high performance, scalability, and cost efficiency.
Monitor, troubleshoot, and improve query performance and warehouse utilization.
Implement data partitioning, clustering, compression, and other optimization strategies.
Establish and enforce best practices for data modeling, transformation, and validation.
Ensure data accuracy, consistency, and timeliness across all downstream systems.
Collaborate with stakeholders to define data requirements and establish a single source of truth.
Partner with analysts, BI developers, and data scientists to deliver reliable datasets for reporting and advanced analytics.
Work with cross-functional teams to identify and prioritize data engineering initiatives.
Document data pipelines, models, and workflows to support transparency and maintainability.
Evaluate and implement new tools, frameworks, and technologies to improve efficiency and scalability.
Drive automation and innovation in data engineering practices.