- about the role

About Us Edge Logistics is a relationship-first freight brokerage that pairs real partnerships with smart technology to move freight for shippers across North America. Founded in 2014, Edge connects shippers of every size to top-tier carriers — balancing cost, capacity, and reliability across full truckload, LTL/partial, temperature-controlled, cross-border, and expedited freight. Edge is headquartered in Phoenix and Chicago with an additional hub in Columbia.

We're hiring a Data Engineer to help design, build, and operate the ELT pipelines and orchestration that move data from our source systems - our on-prem SQL Server TMS (McLeod), AWS-hosted applications, and third-party market and telephony APIs - into Snowflake, and out to downstream BI and AI consumers. You'll work hands-on across AWS (Lambda, ECS, Athena, S3) and Snowflake, and you'll have a real hand in setting the technical standards for how Edge does data engineering going forward. This role is structured as a 12-month engagement with the opportunity to convert to a permanent position based on performance and business need.

Key Responsibilities

- Design, build, and maintain ELT pipelines that land data from on-prem SQL Server (McLeod TMS) and external APIs (market rate/capacity data, carrier and telephony platforms, and others) into Snowflake's raw layer, and transform it through staging/silver into curated/gold, production-ready datasets
- Build and operate transformation logic (dbt or equivalent) to create clean, well-documented, tested models — dimensional/star schemas where appropriate — that downstream teams can trust
- Develop and maintain orchestration for pipeline scheduling, dependency management, retries, and alerting (e.g., EventBridge Scheduler, Airflow, or similar),



so pipelines run reliably without manual babysitting
- Build and maintain AWS-based ingestion infrastructure: containerized Lambda functions, ECS tasks, Athena queries against S3, and supporting services (ECR, Secrets Manager, IAM, SES)
- Establish and enforce data quality, testing, and monitoring practices across pipelines — catching breakages before downstream users do
- Partner with the Director and BI lead to translate the enterprise architecture roadmap into shipped pipelines, models, and documentation
- Support data science and analytics teams by delivering reliable, well-modeled datasets and feature pipelines, and by removing data bottlenecks that slow their work
- Recommend technical approaches grounded in industry best practices, and clearly explain and defend the reasoning behind architectural decisions to technical and non-technical stakeholders
- Continuously look for ways to simplify, automate, and optimize pipelines, queries, and infrastructure for performance, reliability, and cost
- Establish and enforce data governance and access control practices as Edge's Snowflake footprint grows
- Help evaluate and integrate new data sources as new business needs — pricing intelligence, carrier scorecards, load risk models — come online
- Set and document engineering standards (code review, version control, CI/CD, testing) for a data engineering function that is still being built

Requirements





- 3+ years of hands-on data engineering experience building production ELT/ETL pipelines
- Strong SQL skills and solid working proficiency in Python
- Hands-on experience with Snowflake, AWS, and Azure, working with the tools outlined in this posting
- Experience with AWS services commonly used in data pipelines — Lambda, ECS or similar container services, Athena, S3, IAM
- Experience integrating data from REST APIs and relational databases (SQL Server experience a strong plus)
- Experience with a transformation framework (dbt strongly preferred) and dimensional/star-schema modeling
- Familiarity with orchestration tools (Airflow, Step Functions, EventBridge Scheduler, or similar)
- Comfort working in a fast-moving, still-being-built environment — this role will involve real ambiguity and the need to make sound judgment calls
- Strong written communication skills — you'll be documenting pipelines and standards for a team that's still forming
- Experience supporting data science and analytics teams, with an understanding of what they need from a data platform to be productive
- Working knowledge of data engineering industry best practices, with the ability to make solid recommendations and defend the decisions behind them to technical and non-technical stakeholders
- A penchant for efficiency — you automate repetitive work, simplify where you can, and optimize for performance and cost while being able to defend the technical decisions we make

Daniel Scott Divisional Principal Recruitment Consultant

ETail Specialist

- [email protected]

My Other Roles

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