Data Engineer

Promise•Oakland, CA

44d•Hybrid

About The Position

At Promise we rely on data for every decision we make – from product and engineering to operations, finance, and everything in between. As our newest Data Engineer, you’ll build and scale the systems that power data-driven decisions across the company. You’ll work directly with a variety of stakeholders in the business – such as product, engineering, customer success, and finance – to design reliable and understandable data architectures, ship resilient pipelines, and create the foundational data products that drive our strategy and product.

Requirements

Have 5+ years of experience as a data or software engineer building data warehouses, pipelines, and distributed data systems.
Can draw on a variety of modeling techniques to drive sustainable data solutions.
Have deep hands-on expertise with a modern data stack, from ingestion to transformation (dbt, etc.), orchestration (Airflow, etc.), observability, and BI (Looker, Metabase, tc.).
Have operated cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, security and permissioning, and data governance.
Are comfortable writing production-grade code in Python or SQL languages, and integrating with CI/CD and infrastructure-as-code workflows.
Enjoy partnering across disciplines—engineering, product, analytics—to translate messy business requirements into elegant data systems.
Thrive as a self-starter in a fast-moving environment, owning both the technical design and the operational outcomes of your work.

Responsibilities

Own data architecture end-to-end. Every new project we build is slightly different – how do we capture, model, and serve critical business data so it’s both flexible and straightforward.
Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and ensure every teammate has access to the data they need, where they need it.
Enforce data quality at scale. Build tests, monitoring, and reconciliation systems that make datasets observable and anomalies actionable.
Design and maintain scalable data models, decision tools, and analytics infrastructure to empower self-serve analytics and derive insight.
Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, comprehensible data through semantic layers, APIs, and real-time query systems.