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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - **Company:** LOB Corporation - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $152,500.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Data Governance, Data Systems, Data Visualization, Python (Programming Language), Online Analytical Processing, SQL Databases, Tableau (Software), Snowflake, B2b Software, Data Layers, Data Management, Looker Analytics, Software Version Control, Data Pipelines - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/d7f03fbd-14a3-43e6-9424-ec675bed7cb2 ## About the Role * 5+ years of Analytics Engineering experience, including a background in ELT frameworks, OLAP modeling, data visualization, and data governance. * 5+ years of SQL experience: at least one big data warehouse system such as Redshift, Snowflake, or BigQuery. Snowflake preferred. * 3+ years of experience operating live production systems using dbt and Python. dbt Developer certification is a plus. * 3+ years of BI Tool experience: at least one analytics platform such as Omni, Looker, or Tableau. Omni preferred. * 1+ years of experience curating semantic data models to enable conversational BI. * Expertise in modern data visualization techniques: Ability to apply best practices to clearly and insightfully communicate data to drive decisions. * Empathy and effective communication skills: Ability to explain complex analytical issues to both technical and non-technical audiences. * Strong interpretive skills: Ability to deconstruct complex source data to compose curated models that can be explored by stakeholders. * Product mindset: Ability to build data systems that will be used to generate insights for years to come, not just one-off analyses., * A four-year degree in an analytical discipline, or equivalent real-world experience is required. * 5+ years of Analytics Engineering experience required. * Domain knowledge in Supply Chain, Finance, B2B SaaS, or a similar relevant field is a plus. ## Description As a member of the Data team, you will develop data pipelines and build data models that enable the entire company to discover actionable insights and make rapid data-driven decisions. You will work with colleagues in Product and Engineering, as well as stakeholders across teams such as Sales, Finance, Marketing, and Operations to identify data needs and develop durable solutions that help answer business questions., * Partner with stakeholders to identify problems, create insights, and develop durable data solutions. * Build robust data pipelines to curate analytical data from internal and external systems. * Curate the semantic data layer to bridge raw tables and business context and enable AI use cases. * Develop dashboards and alerting systems that track and monitor business performance, with a focus on clear and insightful data visualizations. * Exemplify analytics engineering best practices, such as modularity, testing, cataloging, and version control. * Champion data governance, security, privacy, and retention policies to protect end users, customers, and Lob. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/videos/2055-why-your-ai-agent-keeps-hallucinating-your-data-building-deterministic-context-layers) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Making Data Warehouses fast. 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