Senior Data Engineer

Department: Technology & Automation (Engineering) Location: Hybrid — Southern California / Remote Role Overview You will architect and evolve a Unified Data Fabric — the core platform that powers operational systems today and enables future customer-facing data products. This role owns the full data lifecycle: extraction, replication, modeling, transformation, and quality across warehouse management, transportation, billing, and ERP systems. Your work will directly shape both internal decision-making systems and the future of a multi-tenant, auditable, and increasingly conversational data platform. Key architectural decisions around modeling, lineage, access control, and scalability will have long-term product impact. You will also use AI tooling as a core part of your engineering workflow — not as autocomplete, but as a research and development partner — while maintaining strong validation standards for LLM outputs. Key Responsibilities End-to-End Data Pipelines Design and build cloud-native pipelines (Azure / Snowflake) to ingest and integrate data from systems such as CargoWise, ProfitTools, Blue Yonder, and NetSuite. Unified Data Modeling Use dbt to clean, standardize, and model complex 3PL datasets into trusted Gold Layer models representing operational truth. Data Quality by Design Embed data quality into every pipeline: Data freshness monitoring Automated reconciliation testing Data Governance & Observability Implement automated checks to detect cross-system inconsistencies (e.g., TMS vs WMS) and establish lineage, auditability, and data trust frameworks for downstream use cases. Enable AI Automation Provide reliable, well-structured datasets and API integrations that support real-time AI-driven operational workflows and decisioning. Required Skills & Experience Strong SQL and Python expertise with production-level experience Experience integrating and reconciling multiple enterprise systems (e.g., TMS, WMS, ERP) Deep experience with modern data stack tools, especially dbt. Strong understanding of data modeling principles (star schema, conformed dimensions, Type 2 SCD, grain design) Experience building production pipelines with embedded data quality controls Infrastructure-as-Code experience (Terraform required; Bicep or Pulumi acceptable) Ability to independently own data systems end-to-end in small or lean engineering teams Strong systems thinking across operational domains and upstream/downstream data impact Practical experience using AI tools as part of engineering workflows (beyond basic LLM usage) Strong Plus Experience with Azure data stack (ADF, ADLS Gen2, Key Vault, Synapse) Background in logistics, supply chain, or 3PL environments Snowflake experience Additional Note This is a direct hire position. We are currently unable to provide visa sponsorship. #J-18808-Ljbffr Kelly