{"id":1981794,"url":"https://alion.io/job/mclane-company-data-engineering-manager","title":"Data Engineering Manager","company":{"id":1904902,"name":"McLane Company","domain":"mclaneco.com","url":"https://alion.io/company/mclaneco","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":76,"open_postings":63,"ghost_share":0,"stale_share":0.937,"repost_share":0.016,"time_to_fill_p50_days":29,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Temple, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":197000,"max_usd":394000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1142},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Alation","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure DevOps","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Data Vault","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"FinOps","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"Incident Management","optional":false},{"name":"Least Privilege","optional":false},{"name":"Looker","optional":false},{"name":"Master Data Management","optional":false},{"name":"MLFlow","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"Zero Trust","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-10-06T23:56:37Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-12T06:29:07Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Take your career further with McLane!\nMcLane 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.\nLead 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.\nThis 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.\nBenefits you can count on:\nDay 1 Benefits: medical, dental, and vision insurance, FSA/HSA, and company-paid life insurance\nPaid time off begins day one.\n401(k) Profit Sharing Plan after 90 days.\nAdditional benefits: pet insurance, maternity/paternity leave, employee assistance programs, discount programs, tuition reimbursement program, and more!\nWhat You'll Do:\nLead, coach, and develop a high performing data engineering organization (employees and partners), fostering a culture of inclusion, accountability, and continuous improvement. \nOwn the architecture and roadmap for cloud based data lake, lakehouse, and warehouse platforms (e.g., Databricks, Snowflake, Synapse, BigQuery). \nEstablish 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. \nDeliver domain oriented data products with clear ownership, SLAs/SLOs, documentation, and measurable business value. \nEnable advanced analytics and AI use cases across demand and supply planning, inventory optimization, transportation, manufacturing, procurement, and fulfillment. \nIntegrate complex enterprise data sources including ERP (SAP/S4), WMS, TMS, OMS, PLM, EDI, vendor portals, IoT/telematics, and 3PL/4PL feeds. \nPartner with Analytics and Data Science teams to deliver feature pipelines, experiment telemetry, and production inference data supporting ML and GenAI initiatives\nLead data governance execution in partnership with business data stewards, including cataloging, lineage, glossary, and ownership (e.g., Purview, Collibra, Alation). \nImplement automated data quality frameworks (e.g., dbt tests, Great Expectations), defining remediation workflows and quality SLAs. \nEnsure compliance with SOX, privacy (GDPR/CCPA), security, and retention requirements, applying least privilege and zero trust access patterns. \nOwn run the business operations, including on call rotations, incident management, post incident reviews, and change control. \nEstablish observability and reliability metrics (SLIs/SLOs, error budgets) for data products and platforms. \nDrive FinOps discipline, tracking cost per pipeline and data product while optimizing compute, storage, and licensing spend. \nManage vendor relationships, SOWs, and managed services, holding partners accountable to delivery, quality, and financial commitments.\nPerform other duties as assigned.\nWhat We're Looking For:\nBachelor’s degree in computer science, engineering, or related field.\nThree 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. \nProven delivery of cloud data platforms supporting analytics and operational workloads in complex, regulated environments. \nStrong hands on expertise with SQL, Python or Scala, and CI/CD & IaC for data platforms (e.g., Terraform, GitHub Actions, Azure DevOps).\nDemonstrated experience implementing data governance, security, lineage, and quality frameworks. \nPractical supply chain domain fluency across two or more areas (planning, inventory, logistics, manufacturing, procurement).\nExperience with Databricks, Snowflake, Synapse, dbt, Airflow/ADF, Kafka/Event Hubs, Delta/Parquet/Iceberg, Apache Beam, Spark/PySpark. \nFamiliarity with Master Data Management (MDM), data contracts, semantic layers, and governed BI platforms (Power BI, Tableau, Looker). \nExposure to ML feature stores, MLflow, and production ML/GenAI enablement.\nUnderstanding of SRE inspired data reliability and observability practices.\nWhy Join Us\nOwn mission critical enterprise communications at scale.\nMake visible impact on reliability, cost control, and executive confidence.\nWork with trusted partners, modern platforms, and complex networks.\nBe recognized as a go to expert and operational leader.\nGrow your career through mentorship, influence, and continuous learning.\nFit the following? We want you here!\nTeamwork oriented\nOrganized\nProblem solver\nDetailed\nOur roadmap. Our story.\nWe’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.\nCandidates may be subject to a background check and drug screen, in accordance with applicable laws.\nAll 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.\nFor our complete EEO and Pay Transparency statement, please visit https://www.mclaneco.com/legal/employment/\nTake your career further with McLane!\nMcLane 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.\nLead 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.\nThis 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.\nBenefits you can count on:\nDay 1 Benefits: medical, dental, and vision insurance, FSA/HSA, and company-paid life insurance\nPaid time off begins day one.\n401(k) Profit Sharing Plan after 90 days.\nAdditional benefits: pet insurance, maternity/paternity leave, employee assistance programs, discount programs, tuition reimbursement program, and more!\nWhat You'll Do:\nLead, coach, and develop a high performing data engineering organization (employees and partners), fostering a culture of inclusion, accountability, and continuous improvement. \nOwn the architecture and roadmap for cloud based data lake, lakehouse, and warehouse platforms (e.g., Databricks, Snowflake, Synapse, BigQuery). \nEstablish 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. \nDeliver domain oriented data products with clear ownership, SLAs/SLOs, documentation, and measurable business value. \nEnable advanced analytics and AI use cases across demand and supply planning, inventory optimization, transportation, manufacturing, procurement, and fulfillment. \nIntegrate complex enterprise data sources including ERP (SAP/S4), WMS, TMS, OMS, PLM, EDI, vendor portals, IoT/telematics, and 3PL/4PL feeds. \nPartner with Analytics and Data Science teams to deliver feature pipelines, experiment telemetry, and production inference data supporting ML and GenAI initiatives\nLead data governance execution in partnership with business data stewards, including cataloging, lineage, glossary, and ownership (e.g., Purview, Collibra, Alation). \nImplement automated data quality frameworks (e.g., dbt tests, Great Expectations), defining remediation workflows and quality SLAs. \nEnsure compliance with SOX, privacy (GDPR/CCPA), security, and retention requirements, applying least privilege and zero trust access patterns. \nOwn run the business operations, including on call rotations, incident management, post incident reviews, and change control. \nEstablish observability and reliability metrics (SLIs/SLOs, error budgets) for data products and platforms. \nDrive FinOps discipline, tracking cost per pipeline and data product while optimizing compute, storage, and licensing spend. \nManage vendor relationships, SOWs, and managed services, holding partners accountable to delivery, quality, and financial commitments.\nPerform other duties as assigned.\nWhat We're Looking For:\nBachelor’s degree in computer science, engineering, or related field.\nThree 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. \nProven delivery of cloud data platforms supporting analytics and operational workloads in complex, regulated environments. \nStrong hands on expertise with SQL, Python or Scala, and CI/CD & IaC for data platforms (e.g., Terraform, GitHub Actions, Azure DevOps).\nDemonstrated experience implementing data governance, security, lineage, and quality frameworks. \nPractical supply chain domain fluency across two or more areas (planning, inventory, logistics, manufacturing, procurement).\nExperience with Databricks, Snowflake, Synapse, dbt, Airflow/ADF, Kafka/Event Hubs, Delta/Parquet/Iceberg, Apache Beam, Spark/PySpark. \nFamiliarity with Master Data Management (MDM), data contracts, semantic layers, and governed BI platforms (Power BI, Tableau, Looker). \nExposure to ML feature stores, MLflow, and production ML/GenAI enablement.\nUnderstanding of SRE inspired data reliability and observability practices.\nWhy Join Us\nOwn mission critical enterprise communications at 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