Manager, Data Engineering - Hybrid Schedule

Odyssey•Charlotte, NC

2d•Hybrid

About The Position

The Data Engineering Manager leads Odyssey’s enterprise data engineering strategy, platform development, and delivery of scalable data solutions that support operational visibility, automation, analytics, and AI-enabled decision-making. This role manages a team of data and analytics engineers while partnering closely with business, operations, product, and technology stakeholders to translate complex logistics challenges into trusted, actionable data products. The position is critical to maturing Odyssey’s modern data platform and advancing a more data-driven organization.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
5+ years of experience building and supporting data platforms, analytics, business intelligence, or data engineering solutions.
2+ years of leadership and management experience.
Strong technical expertise in data architecture, data modeling, cloud platforms, and modern data engineering practices.
Experience with Databricks, AWS, Snowflake, Airflow, Sigma Computing, or comparable tools and platforms.
Proven ability to lead large-scale data initiatives that support business goals and improve decision-making.
Strong collaboration and stakeholder management skills, with the ability to align technical solutions to business needs.
Excellent leadership, problem-solving, communication, and prioritization skills.

Nice To Haves

Logistics, transportation, or supply chain experience preferred

Responsibilities

Lead the architecture and development of Odyssey’s enterprise data lake to improve logistics operations, real-time tracking, supply chain optimization, and customer-facing analytics.
Partner with business, operations, and product stakeholders to identify high-impact data initiatives, prioritize delivery, and translate logistics use cases into scalable technical solutions.
Own and evolve the modern data stack, including Databricks, Delta Lake, Sigma Computing, Snowflake, Airflow, and AWS.
Oversee ELT/ETL pipelines integrating data from TMS, WMS, telematics, EDI feeds, and IoT sensors.
Deliver trusted, reusable, high-quality data products that support self-service dashboards, operational reporting, and embedded analytics.
Implement best practices for data observability, pipeline orchestration, CI/CD, performance optimization, scalability, and resilience.
Champion data governance, lineage, and quality standards aligned with GDPR, SOX, customer SLAs, and internal standards.
Manage sprint planning, agile execution, resource allocation, and team development.
Collaborate with vendors and external partners on tool adoption, integrations, and roadmap alignment.
Lead, coach, develop, and retain a high-performing global data engineering team.