Key Responsibilities and Required Skills for a Freight Engineer

🎯 Role Definition

As a Freight Engineer, you are the architect and builder of the data-driven engine that powers our logistics and supply chain operations. You will be instrumental in transforming raw, complex freight data into actionable intelligence. This role involves designing, developing, and maintaining scalable data pipelines, platforms, and services that support everything from real-time shipment tracking and carrier performance analysis to pricing optimization and network modeling. You'll collaborate closely with data scientists, analysts, and product managers to unlock new capabilities and efficiencies, directly impacting the movement of goods across the globe.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Engineer
Software Engineer (with a data or backend focus)
Business Intelligence Engineer
Logistics Analyst (with strong technical skills)

Advancement To:

Senior or Staff Freight Engineer
Data Engineering Manager
Principal Data Architect
Technical Lead (Data Platform)

Lateral Moves:

Data Scientist (Logistics & Supply Chain)
Product Manager (Technical or Data Products)
Solutions Architect (Logistics Technology)

Core Responsibilities

Primary Functions

Design, construct, and maintain robust, scalable ETL/ELT pipelines to process high-volume, real-time freight and logistics data from diverse sources like APIs, EDI feeds, IoT sensors, and third-party systems.
Develop and optimize complex data models within our data warehouse (e.g., Snowflake, Redshift, BigQuery) to support analytical dashboards, reporting, and machine learning initiatives.
Build and manage data ingestion frameworks to reliably capture and validate structured and unstructured data related to shipments, carriers, pricing, and transit times.
Engineer data-centric microservices and APIs that provide other engineering and product teams with clean, reliable access to core freight and supply chain data.
Implement advanced data quality checks, monitoring, and alerting systems to ensure the accuracy, completeness, and timeliness of our critical logistics datasets.
Collaborate with data scientists to productionize machine learning models for applications such as transit time prediction, route optimization, and dynamic pricing.
Architect and evolve our data infrastructure on cloud platforms (AWS, GCP, or Azure), leveraging services like S3, Glue, Kinesis, EMR, and Redshift to enhance performance and scalability.
Create and maintain comprehensive documentation for data pipelines, data models, and engineering processes to foster knowledge sharing and efficient onboarding.
Lead the technical design and implementation of new data products and features that provide a competitive advantage in the freight marketplace.
Proactively identify and resolve performance bottlenecks in data processing jobs and database queries to ensure service level agreements (SLAs) are consistently met.
Write clean, maintainable, and well-tested Python and SQL code, adhering to software engineering best practices and participating in rigorous code reviews.
Automate manual data processes to improve operational efficiency, reduce human error, and enable the operations team to focus on higher-value activities.
Integrate data from various transportation management systems (TMS), carrier portals, and freight visibility platforms into a unified central data lake or warehouse.
Develop data partitioning and clustering strategies to significantly improve query performance and reduce computational costs in our big data environment.
Work with product managers and business stakeholders to understand complex requirements and translate them into technical specifications for the data platform.
Ensure all data handling and processing complies with data governance policies and industry regulations for security and privacy.
Build frameworks for A/B testing and experimentation to measure the impact of new pricing algorithms, carrier strategies, or operational changes.
Own the end-to-end lifecycle of critical freight datasets, from ingestion and processing to consumption by downstream applications and users.
Mentor junior engineers on the team, providing technical guidance and promoting a culture of engineering excellence.
Drive the adoption of new technologies and best practices in the data engineering space to keep our platform modern and efficient.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to answer pressing business questions from operations, finance, and sales teams.
Contribute to the organization's broader data strategy and technology roadmap, advocating for improvements and new capabilities.
Collaborate with business units to deeply understand their challenges and translate those data needs into actionable engineering requirements.
Participate actively in sprint planning, daily stand-ups, and retrospective agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: Expertise in writing complex, highly-optimized SQL queries and working with relational database concepts.
Python Programming: Strong proficiency in Python for data manipulation (Pandas, NumPy), automation, and building data applications.
ETL/ELT & Data Pipeline Orchestration: Hands-on experience with tools like Apache Airflow, Dagster, Prefect, or similar workflow management systems.
Big Data Technologies: Proficiency with distributed computing frameworks such as Apache Spark, Flink, or Dask.
Cloud Data Platforms: Deep experience with at least one major cloud provider's data stack (e.g., AWS: S3, Glue, Redshift, EMR; GCP: BigQuery, Dataflow, Composer; Azure: Data Factory, Synapse).
Data Warehousing & Modeling: Strong understanding of dimensional modeling, data lake architecture, and experience with modern cloud data warehouses like Snowflake, BigQuery, or Redshift.
Streaming Data Processing: Experience with real-time data streaming technologies like Kafka, Kinesis, or Pulsar is a significant plus.
Infrastructure as Code (IaC): Familiarity with tools like Terraform or CloudFormation for managing cloud resources.
Containerization: Knowledge of Docker for building and deploying applications consistently.
Version Control: Proficient use of Git for collaborative code development.

Soft Skills

Problem-Solving: Ability to tackle complex, ambiguous problems with a structured and analytical approach.
Business Acumen: A strong interest in or direct experience with the logistics, freight, or supply chain industry.
Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Collaboration: A team-player mindset with a proven ability to work effectively in a cross-functional environment.
Ownership & Accountability: A proactive, self-starting attitude with a strong sense of ownership for the quality and impact of your work.
Attention to Detail: Meticulous and detail-oriented, especially concerning data quality and accuracy.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree in Computer Science, Engineering, Statistics, or another quantitative field, or equivalent practical experience.

Preferred Education:

Master's Degree in Computer Science, Data Science, Data Engineering, or a related technical discipline.

Relevant Fields of Study:

Computer Science
Data Science
Operations Research
Industrial Engineering
Supply Chain Management

Experience Requirements

Typical Experience Range: 3-7 years of professional experience in a data engineering, backend engineering, or similar role.

Preferred: Direct experience working within the freight, transportation, logistics, or supply chain industry is highly desirable and will be a strong differentiator.