Sr Data Engineer

Echo Global Logistics•Chicago, IL

9d•$112,498 - $163,571•Remote

About The Position

Echo Global Logistics is a leading provider of technology-enabled transportation management services. As a third-party logistics provider, we simplify transportation management for our clients and carriers, handling crucial tasks so they can focus on what they do best. From coast to coast, dock to dock, and across all major transportation modes, Echo connects businesses that need to ship their products with carriers who transport goods quickly, securely, and cost-effectively. This role involves collaborating with Agile teams to design, develop, integrate, and manage large-scale data solutions, including data storage, data modeling, ETL, data querying, and reporting. The position emphasizes delivering robust cloud-based solutions, researching and building relational and non-SQL solutions, and utilizing programming languages like Python, Open Source and NoSQL databases, and various AWS tools and services. A significant aspect of the role is AI-Augmented Development, which includes defining and leading the adoption of GenAI coding workflows using tools like GitHub Copilot, Cursor, and OpenAI APIs, designing agentic workflows, and establishing prompt engineering standards. The role is outcome-driven, requiring ownership of complex data products from design to production, focusing on business metrics, designing high-performance transformation architecture, and driving productivity improvements through automation. Additionally, the position requires AI Governance & Quality Oversight, acting as a human-in-the-loop to review AI-generated code, applying security awareness, and conducting architectural and code reviews.

Requirements

At least 5 years of professional data or software engineering experience (Internship experience does not apply)
5+ years of experience in at least 2 of the following modern languages: .Net, Python, Scala, Java
3+ years of experience with AWS, GCP, Azure, or another cloud service
2+ years of experience in Agile practices
Experience with AWS Athena and Redshift
Experience with reporting tools such as Quicksuite, PowerBI, Tableau
Proven experience leading technical design, architecture decisions, and cross-team technical initiatives
Demonstrated experience mentoring and coaching engineers
Active, hands-on proficiency with at least one AI coding assistant (GitHub Copilot, Cursor, or equivalent)

Nice To Haves

7+ years of professional data or software engineering experience
Bachelor's Degree in computer science or equivalent years of experience.
AWS Certifications (Solutions Architect Professional or Developer Professional)
Experience in logistics, transportation, or supply chain technology domains
Experience with observability platforms, distributed tracing, and SRE practices
Track record leading platform migrations, modernization initiatives
Strong background in event-driven Data Lake architectures

Responsibilities

Collaborate with Agile teams to design, develop, integrate, and manage Echo's large scale data solutions including data storage, data modeling, ETL, data querying, and reporting
Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the data engineering community
Deliver robust cloud-based solutions.
Lead in the research, evaluation, operational usage and building of relational and non SQL solutions
Utilize programming languages like Python, Open Source and NoSQL databases, and a variety of AWS tools and services such as AWS Glue, Firehose, S3, Redshift and Athena
Define and lead adoption of GenAI coding workflows using tools such as GitHub Copilot, Cursor, and OpenAI APIs - actively guiding, not passively accepting, AI-generated output.
Design and operationalize agentic, multi-step workflows to automate feature development, bug triage, and documentation pipelines.
Establish prompt data engineering standards, validation frameworks, and team playbooks that ensure consistent, high-quality AI-assisted development across the organization.
Own complex data products end-to-end - from design to production - with a clear line of sight to business metrics such as cycle time, defect rates, and system uptime.
Design high-performance transformation architecture.
Drive measurable productivity improvements by replacing manual effort with intelligent automation at every stage of the SDLC.
Act as the critical human-in-the-loop - rigorously reviewing AI-generated code for hallucinations, security vulnerabilities, architectural gaps, and logical flaws before they reach production.
Apply security awareness across AI tooling (OWASP Top 10 and AI-specific attack surfaces) and enforce standards that protect system integrity.
Conduct architectural reviews and code reviews that elevate both human-written and AI-generated contributions to production quality.