Data Engineering Manager

McLane teammates, the driving force behind our success, are diverse professionals who work together seamlessly to keep our operations running smoothly. As a teammate, you will pair your dedication, expertise, and collaborative spirit with your fellow teammates to serve America’s most beloved brands. McLane leaders think long-term, act with purpose, and inspire high performance. They lead with accountability, communicate clearly, and drive results through collaboration, innovation, and continuous growth. They empower each teammate to learn from industry leaders, develop their skills, and build lasting connections nationwide.

Lead the design, delivery, and operation of enterprise-scale data platforms and data products that power end to end supply chain planning, execution, and optimization. Own the full data engineering lifecycle, from ingestion and transformation to modeling, serving, and operational reliability, enabling analytics, BI, AI/ML, and operational decision making across planning, procurement, manufacturing, logistics, fulfillment, and last mile delivery. Ensure data assets are trusted, secure, compliant, cost efficient, and AI ready, supporting executive level decisioning and operational excellence at scale through people leadership, modern data platform ownership, digital supply chain transformation and data governance.

This is a hybrid position which will require the candidate to report and work from the office at least three days a week. Therefore, interested candidates should be within a 50-minute radius from Temple, TX.

• →Lead, coach, and develop a high performing data engineering organization (employees and partners), fostering a culture of inclusion, accountability, and continuous improvement. 
• →Own the architecture and roadmap for cloud based data lake, lakehouse, and warehouse platforms (e.g., Databricks, Snowflake, Synapse, BigQuery). 
• →Establish scalable patterns for ELT/ETL pipelines, orchestration (AirFlow/Cloud Composer), data modeling (Kimball, Data Vault 2.0, wide tables),BigQuery SQL transformations and API or streaming data delivery using technologies such as Dataflow (Apache Beam) and Spark/PySpark. 
• →Deliver domain oriented data products with clear ownership, SLAs/SLOs, documentation, and measurable business value. 
• →Enable advanced analytics and AI use cases across demand and supply planning, inventory optimization, transportation, manufacturing, procurement, and fulfillment. 
• →Integrate complex enterprise data sources including ERP (SAP/S4), WMS, TMS, OMS, PLM, EDI, vendor portals, IoT/telematics, and 3PL/4PL feeds. 
• →Partner with Analytics and Data Science teams to deliver feature pipelines, experiment telemetry, and production inference data supporting ML and GenAI initiatives
• →Lead data governance execution in partnership with business data stewards, including cataloging, lineage, glossary, and ownership (e.g., Purview, Collibra, Alation). 
• →Implement automated data quality frameworks (e.g., dbt tests, Great Expectations), defining remediation workflows and quality SLAs. 
• →Ensure compliance with SOX, privacy (GDPR/CCPA), security, and retention requirements, applying least privilege and zero trust access patterns. 
• →Own run the business operations, including on call rotations, incident management, post incident reviews, and change control. 
• →Establish observability and reliability metrics (SLIs/SLOs, error budgets) for data products and platforms. 
• →Drive FinOps discipline, tracking cost per pipeline and data product while optimizing compute, storage, and licensing spend. 
• →Manage vendor relationships, SOWs, and managed services, holding partners accountable to delivery, quality, and financial commitments.
• →Perform other duties as assigned.

• Bachelor’s degree in computer science, engineering, or related field.
• Three or more years of experience in data engineering or closely related roles, with three or more years leading engineers or technical programs at enterprise scale. 
• Proven delivery of cloud data platforms supporting analytics and operational workloads in complex, regulated environments. 
• Strong hands on expertise with SQL, Python or Scala, and CI/CD & IaC for data platforms (e.g., Terraform, GitHub Actions, Azure DevOps).
• Demonstrated experience implementing data governance, security, lineage, and quality frameworks. 
• Practical supply chain domain fluency across two or more areas (planning, inventory, logistics, manufacturing, procurement).
• Experience with Databricks, Snowflake, Synapse, dbt, Airflow/ADF, Kafka/Event Hubs, Delta/Parquet/Iceberg, Apache Beam, Spark/PySpark. 
• Familiarity with Master Data Management (MDM), data contracts, semantic layers, and governed BI platforms (Power BI, Tableau, Looker). 
• Exposure to ML feature stores, MLflow, and production ML/GenAI enablement.
• Understanding of SRE inspired data reliability and observability practices.

• Teamwork oriented
• Organized
• Problem solver
• Detailed

We’ve been forging our path as a leader in the distribution industry since 1894. Building an expansive nationwide network of team members for 130+ years has allowed us to stay agile for our clients across the restaurant, retail, and e-commerce industries. We look to the future and are ready to continue making industry-defining moves by embracing the newest technology into our practices, continuing team member training, and emphasizing our people-centered culture.  

Candidates may be subject to a background check and drug screen, in accordance with applicable laws.  

All applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

For our complete EEO and Pay Transparency statement, please visit https://www.mclaneco.com/legal/employment/