Staff/Senior Data Engineer

The Data Engineer in will own the design, implementation, and scaling of our analytics infrastructure to form the foundation of a cohesive analytics ecosystem. This role also empowers analytics engineers and data scientists to follow DataOps best practices to independently create metrics. Additionally, the Data Engineer will interface with the application database team to synergistically improve the overall cloud ecosystem, enabling scalable ways to share large volumes of data with clients. The ideal candidate will have demonstrable experience implementing data warehouses, a strong foundation in SQL and Python, and a mindset for cloud systems design. Leveling (Senior or Staff) will be determined through the interview process based on your background and technical depth.

San Francisco Bay Area (Hybrid — Burlingame office 2-3x/week required) Must currently reside locally; this is not a remote-eligible role.

Simbe Robotics is a leading retail robotics company providing in-store intelligence solutions that help retailers optimize operations, improve shelf execution, and deliver valuable data insights. Our autonomous robots and multi-modal data collection systems are transforming how retailers manage inventory and make data-driven decisions.

The base salary offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include other elements, including equity compensation, in addition to a full range of medical, financial, and/or other benefits.

Simbe's approach emphasizes total rewards - base pay, equity, incentives, and benefits - rather than viewing compensation as cash alone. We believe the full package, including ownership through equity and well-being support, is what drives engagement, retention, and alignment with our mission.

Data Warehousing Ingest data from Postgres, Google Cloud Storage, Jira, web analytics platforms, and other sources into BigQuery. Use Data Build Tool (dbt) to transform raw data into user friendly “data-marts” for use by AI and BI tools.

External Data Exchange Guide the organization on how data is ingested from and delivered to clients. Establish the infrastructure for scalable and traceable inbound feeds (product catalogs, planograms) and large outbound delivery (reporting extracts, cloud-native data sharing)

Optimizing and Monitoring Cloud Costs Monitor and implement controls to ensure data warehousing costs are predictable and within reasonable levels. Unlock synergies between the application database and data warehouse to deliver timely insights and reduce overall cloud costs.

Data Quality & Governance Use dbt to establish a robust data testing framework that ensures data remains accurate and trustworthy, while also producing a comprehensive suite of metric definitions and documentation accessible to anyone in the organization. Champion DataOps best practices across teams to cultivate a consistent, high-quality approach to data management and analytics.

Security & Compliance Help ensure account permissions remain appropriate and enforced across the data consumption layer. Implement controls to ensure the data warehouse maintains compliance with GDPR, PII laws, SOC 2, and other regulations

Proven experience implementing and owning data warehouses end-to-end

Track record of data modeling for analytical use cases

Background empowering peers through DataOps best practices

Strong emphasis on data security and governance

Deep understanding of the strengths and weaknesses of transactional vs. analytical databases