Senior Data Engineer

About iDC Logistics iDC Logistics, founded in 2004 by logistics industry veterans, is a leading end-to-end logistics company focused on transparency, efficiency, and innovative supply chain solutions. Headquartered in Los Angeles, iDC provides integrated services including freight forwarding, contract logistics, transportation, and distribution management for retailers worldwide. With expertise across consumer electronics, industrial products, and healthcare industries, iDC Logistics delivers customized logistics solutions through operational excellence, technology-enabled processes, and customer-focused service. Our success is driven by a commitment to customer satisfaction, employee engagement, and continuous innovation. Position Summary The Senior Data Engineer owns the operation of that platform as it moves into production: environments, database administration, security roles and grants, refresh and orchestration, monitoring, deployment and CI. The role also delivers well-scoped production data work within existing patterns, so that architecture and design work can continue in parallel. Platform architecture, the security model and the data roadmap are owned by the Principal Data Engineer. This role holds deep, autonomous ownership beneath that architecture, primarily the operational and database-administration side of the platform. Key Responsibilities Production operations and platform health. Own environment setup and management, refresh and orchestration operations, monitoring and alerting, and incident response for a platform supporting production report delivery and billing. Database administration. Manage security-role hierarchies, grants, performance and maintenance. Deployment and CI. Maintain pipeline automation and environment promotion so that releases are routine and reliable. Modeling and transformation delivery. Build staging and gold dbt models, with tests, against defined business semantics and existing conventions. Flag requests that would conflict with established definitions. Code review. Review teammates’ work, including AI-generated code, for correctness and adherence to standards. Reverse-engineering source systems. Analyze and absorb legacy and vendor source systems, many with undocumented schemas, as a recurring part of the role. Report and billing parity. Validate that new platform outputs match the legacy system’s reporting and billing figures before cutover. Required Qualification Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field. 7+ years of experience designing, developing, and maintaining large-scale data pipelines and data warehouse solutions. SQL at working depth: joins, window functions, grain and aggregation tradeoffs, NULL semantics, and the ability to reason about why a query returns what it returns. Production Python: has written and maintained Python code that ran in production. Pipeline experience: has owned scheduled data pipelines in production, including diagnosing and fixing failures. Transformation framework: working depth in at least one modern framework. dbt preferred; SQLMesh, Dataform, Coalesce, Databricks declarative pipelines or a comparable in-house framework also qualify. Production operations: responsibility for running systems, including deployments, environment management, on-call or equivalent, and incident response. Dimensional modeling: grain, facts and dimensions, conformance and slowly-changing history. Git-based workflow: branching, pull requests, code review and CI as standard practice. Communication: clear in writing, in conversation and in presentations, including explaining technical findings to non-technical stakeholders. Independent problem-solving: able to investigate unfamiliar problems substantially before escalating. AI tooling: uses AI tools as a regular part of engineering work for research, design and verification; has built tooling for their own workflow; verifies AI output before re…

View the full posting and apply