Staff/Senior Data Engineer

Simbe Robotics•San Francisco, CA

8d•$135,000 - $170,000•Hybrid

About The Position

The Data Engineer will own the design, implementation, and scaling of Simbe Robotics' analytics infrastructure to form the foundation of a cohesive analytics ecosystem. This role empowers analytics engineers and data scientists to follow DataOps best practices to independently create metrics. The Data Engineer will also 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. The leveling for this role (Senior or Staff) will be determined during the interview process based on the candidate's background and technical depth.

Requirements

Bachelor's or master's degree in Computer Science or related field highly recommended, not required
5+ years of experience with SQL, BigQuery, Terraform, Apache Beam, Airflow, and GCP
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
Excellent communication skills; able to convey complex technical concepts across cross-functional teams
Autonomous and independent, with strong ownership and accountability
Able to evangelize and champion best practices, ensuring alignment and consistent adoption across the org

Nice To Haves

Hands-on expertise with dbt and event-driven analytics highly desirable

Responsibilities

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.
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).
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.
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.
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.