Manager, Data Engineering

Pilot Thomas Logistics; Houston (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

A logistics company in Texas is seeking a Manager of Data Engineering to lead its data initiatives. This hands-on role involves managing a small team to enhance the data platform and analytics capabilities. The ideal candidate should have substantial experience in data engineering, proficient in SQL and Python, and strong leadership skills. Responsibilities include defining data strategies, managing cloud environments, and ensuring robust data governance practices.

Qualifications

• 8+ years of experience in data engineering or related fields.
• Hands-on experience with cloud platforms (AWS, Azure, GCP).
• Demonstrated ability to lead technical teams.

Responsibilities

• Lead and mentor a team of data professionals.
• Define the data engineering roadmap aligned with business goals.
• Ensure data procurement and management to support operations.

Skills

SQL expertise

Python development

Leadership skills

Communication skills

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field

Tools

Apache Airflow

Microsoft SQL Server

AWS

Job description

Join to apply for the Manager, Data Engineering role at Pilot Thomas Logistics

We are seeking a strategic, hands‑on Manager, Data Engineering to own and evolve our enterprise data platform and analytics ecosystem. Reporting to the CIO, this role is responsible for defining the data engineering roadmap, leading a small cross‑functional team, and delivering robust, scalable, secure data capabilities that enable self‑service analytics, operational reporting, and AI/automation initiatives.

The Manager مۇ Data Engineering will be both a technical leader and people manager. This is a hands‑on data engineering role. As a leader you will be mentoring individual contributors, partnering with business leaders and vendors for success, and shaping data strategies throughout the business.

• Lead, mentor, and develop a small team of data professionals (Data Engineer, Junior DBA, BI developers), establishing clear goals, career development plans, and engineering best practices.
• Define the short‑and long‑term data engineering roadmap aligned to business priorities and CIO strategy.
• Own the enterprise data architecture across on‑prem and cloud environments (Microsoft SQL Server, AWS/Azure/GCP), ensuring reliability, scalability, and security.
• Design and oversee implementation of a modern data platform (data lake / lakehouse / data warehouse patterns as appropriate) and data modeling standards to support analytics, data automation/integration and AI initiatives.
• Maintain architecture documentation錄 and ensure the environment evolves with business needs.

Data Engineering & Operations

• Ensure key data required to support sales, supply, and operations is procured, maintained, and managed to provide the business with necessary functional information in a timely manner.
• Support team as they architect, build, and operationalize scalable ETL/ELT pipelines using Python, off‑the‑shelf ETL tools, and {. other orchestration tools.
• Set standards for CI/CD, automated testing, monitoring, and alerting for data pipelines and database deployments.
• Establish and enforce backup, عنوان archiving, retention, and disaster recovery strategies for databases and datasets.
< proven>Optimize Serialized databases and queries for reliability, performance, and coût efficiency — including indexing, partitioning, and tuning strategies.

Governance, Security & Quality

• Implement and govern data quality, metadata management, lineage, and cataloging practices.
• Define and enforce data access controls, encryption, and security policies to meet compliance and internal requirements.
• Create SLAs and runbooks for incident response and data platform support.
• Lead AI forward problem solving methodologies to standardize data, accelerate change, and collaborate across business units.

Cross‑Functional Partnership

• Collaborate closely with Software Development, Program Management, and business stakeholders to integrate new applications and third‑party data sources.
• Provide thought leadership and suggest solutions as teams move toward future‑state operations. Promote unified data sets, intelligence layers, and enterprise readiness, and other nimble and creative adaptive solutions.

AI & Automation försöker

• Lead POCs and production rollouts of AI‑driven automation and integrations (ML/AI pipelines, process automation), align solutions with future‑state architecture and process transformation.
• Create and protect PTL Blut Assets that can be used to drive value.eshin participating in governance, compliance, monitoring, and security initiatives to protect data assets and promote customer satisfaction.
• Create an environment where PTL Human Assets can be creative, make good decisions, and move fluidly as fuel markets and strategies evolve.
• Evaluate, onboard, and manage third‑party data tools and cloud services.
• Monitor and manage cloud and platform costs; recommend optimizations and capacity planning.

Other Functions:

• Special projects as requested.
• Perform other duties as assigned.

Required

• Industry experience in downstream fuel logistics or comparable industry knowledge.
• Deep expertise in SQL and relational database design and performance tuning (Microsoft SQL Server required).
• Strong Python development skills and proven experience building ETL/ELT pipelines simplify.
• Experience with orchestration tools (e.g., Apache Airflow) and CI/CD for data workflows.
• Hands‑on experience with at least one major cloud platform (AWS, Azure, or GCP) and a working knowledge of hybrid cloud/on‑prem environments.
• Demonstrated ability to lead technical teams, mentor engineers, and manage cross‑functional initiatives.
• Excellent communication skills and experience translating complex technical ideas to non‑technical stakeholders.

Preferred

• 8+ years of professional experience in data engineering, data architecture, or related roles, with at least 3 years in a leadership or technical lead capacity, highly preferred.
• Familiarity with AI/ML technologies, MLOps, and integrating AI into automation and data pipelines.
• Exposure to containerization (Docker), orchestration rnd (Kubernetes), infrastructure as code (Terraform), and DevOps practices.
• Cloud or data engineering certifications (AWS/Azure/GCP/Data Engineering) a plus.

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.

What Success Looks Like (First 6‑12 Months)

• A documented, prioritized data engineering roadmap aligned with CIO and business objectives.
• Stable, monitored ETL pipelines with CI/CD, automated tests, and clear runbooks; measurable improvements in reliability and pipeline SLAs.
знаменитClear data governance framework, including access controls, quality checks, and documentation of enterprise datasets.
• A growing and engaged data team with established mentorship and development rhythm.
• Tangible progress on AI/automation pilots and integrated data solutions that begin to show business value.

Working Conditions

• Must be able to occasionally lift as much as ten (10) lbs.
• May be required to sit and review information on a computer screen for long periods of time.