About the role
At Neighbor, we’re building the largest hyperlocal marketplace the world has ever seen. We’ve raised over $75 million from top-tier investors such as Andreessen Horowitz and the CEOs of DoorDash, StockX, and Uber. Our marketplace is already flourishing in all 50 states and we’re just getting started! As a Software Engineer, Data , you will be the core engineer responsible for building, scaling, and optimizing the data infrastructure that transforms raw events into high-fidelity, actionable intelligence. You will pioneer a foundation of engineering excellence, enabling the entire organization to make reliable, data-driven decisions at scale across all 50 states. You'll join our data & analytics team as its first dedicated data engineer, working alongside data analysts and data scientists. You'll own the pipelines and transformation layer that power analytics at Neighbor: ingesting data from production databases, APIs, and event streams, and serving it through our semantic layer and BI tools. You'll be the engineering backbone of a small, high-leverage data team. Our stack: dbt and Dagster on Redshift and Athena, with Superset for BI. \n Primary
Responsibilities Design and build robust pipelines to ingest data from diverse sources (APIs, logs, relational DBs). Ensure the reliable and timely execution of all critical data pipelines (ETLs/ELTs) to maintain data integrity and freshness. Standardize analytics workflows by integrating software engineering best practices, including version control, CI/CD pipelines, and automated data validation protocols. Develop and refine a robust semantic layer to facilitate self-service analytics, enabling stakeholders to derive insights without exposure to underlying architectural complexities. Monitor and optimize cloud compute utilization and data model performance to ensure high availability and low-latency reporting during periods of rapid data scaling. Serve as a strategic technical partner to leadership across Product, Engineering, Marketing, and Finance to align data infrastructure with organizational objectives. Become a subject matter expert on the product ecosystem, user behavior, and marketing life cycles to better translate raw data into business value. Go deep on analysis yourself when the question demands it, you're comfortable in both the pipeline and the insight Mentor our data analysts and data scientists on engineering best practices (code review, testing, version control, pipeline design), raising the technical bar across