Senior Data & Analytics Engineer
New
L
Lanter Delivery SystemsLogistics and transportation
Source API remote eligibility restrictions: United StatesFull-TimeSenior
Salary165,000 - 175,000 USD per year
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Job Details
- Experience
- 5+ years of experience in data engineering, analytics engineering, BI engineering, or a related field.
- Required Skills
- PythonSQLGitSparkDatabricksAzure DevOpsPySpark
Requirements
- Have 5+ years of experience in data engineering, analytics engineering, BI engineering, or a related field.
- Have strong, recent, hands-on experience developing production pipelines with Python and Spark/PySpark.
- Bring advanced SQL skills and hands-on experience with Databricks and Azure Data Lake.
- Have experience with Azure DevOps or comparable technologies.
- Have experience designing integrated enterprise models and dimensional data marts, including grain, keys, conformed dimensions, history, and slowly changing dimensions.
- Have experience implementing automated and regression testing, data-quality controls, and pipeline validation.
- Have experience operating production data solutions, including monitoring, optimization, troubleshooting, recovery, and cost management.
- Have experience with Git, code review, CI/CD, reusable development practices, and technical documentation.
- Be able to translate complex business requirements into scalable data and analytics solutions.
- Experience in logistics, transportation, supply chain, or another operationally complex environment is preferred.
- Experience supporting data governance, AI, or advanced analytics initiatives is preferred.
Responsibilities
- Build and operate ETL/ELT pipelines using Databricks, SQL, Python, Spark/PySpark, and Azure Data Lake.
- Develop an integrated enterprise gold-layer model spanning source systems, customers, and operating companies.
- Build dimensional serving-layer and data-mart models, defining grain, keys, conformed dimensions, historical data, and slowly changing dimensions.
- Implement automated regression testing with defined inputs and expected outputs to validate pipelines and transformations.
- Own orchestration, observability, performance, failure diagnosis, replay and recovery, root-cause fixes, and cost management for assigned production solutions.
- Partner with stakeholders to reconcile business definitions and establish data requirements, rules, ownership, and governance standards.
- Apply engineering practices including Git, code review, reusable components, automated testing, controlled deployments, and technical documentation.
- Develop analytics and reporting solutions using Sigma, Power BI, or comparable platforms.
- Support data initiatives across business units and industry verticals, including AI, operational visibility, and analytics enablement.
- Own deliverables independently and provide technical guidance on complex data and analytics needs.
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