Data Engineer

Horizon Air Freight

20d

About The Position

Key Responsibilities: Data Pipeline Development & Integration 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). Data Warehouse Management Maintain and optimize our Amazon Redshift 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. Data Quality & Governance 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. Collaboration & Stakeholder Support 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. Continuous Improvement Evaluate and implement new tools, frameworks, and technologies to improve efficiency and scalability. Drive automation and innovation in data engineering practices.

Nice To Haves

Experience with Amazon Redshift 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 Amazon Redshift 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.