Senior Data Engineer

E2B•San Francisco, CA

2d•$200,000 - $250,000•Hybrid

About The Position

E2B is seeking a Senior Data Engineer to construct the data infrastructure essential for business understanding and decision-making. The role involves transforming data from various systems into reliable pipelines and well-defined datasets. Reporting to the Head of Engineering, this position will initially partner closely with the Head of Finance, with future collaboration across Growth, GTM, and other departments. The engineer will be responsible for integrating existing data, establishing new foundational elements, and ensuring the quality and utility of the data delivered. This role offers direct access to data users and significant autonomy in shaping E2B's data foundation, enabling teams to answer questions independently, reduce time spent on data reconciliation, and make informed decisions.

Requirements

Experience building and owning production data pipelines and warehouse models, including establishing foundations where systems were incomplete or fragmented.
Strong SQL and Python skills, with sound software engineering practices around testing, version control, deployment, and monitoring.
Experience with a cloud data warehouse such as BigQuery, Snowflake, or Redshift, and tools for data transformation and orchestration.
A track record of working directly with business stakeholders, clarifying ambiguous requirements, and connecting technical work to business outcomes.
The ability to investigate discrepancies, understand their root causes, and make the underlying systems more reliable.
Strong ownership: you can identify what needs to happen, prioritize it, and carry it through without detailed instructions.
Approximately six or more years of relevant work experience, with emphasis on depth of ownership and built systems.

Nice To Haves

Building data foundations at a startup or on a small team.
Supporting Finance, Revenue Operations, Growth, or GTM.
Working with usage-based products, billing data, or cloud infrastructure costs.
Building well-documented datasets that support self-service analytics and AI tools.
Finance domain expertise.
Curiosity about the business, ability to learn quickly, and taking responsibility for delivering trustworthy data.

Responsibilities

Build and operate pipelines that bring operational and business data into our warehouse, including sandbox usage, cloud infrastructure, billing, and accounting data.
Create data models and shared metric definitions that teams can understand and use consistently.
Establish testing, monitoring, reconciliation, and alerting to catch missing data, inconsistencies, and unexpected changes.
Partner with Finance to turn business questions into dependable datasets, starting with usage, revenue, costs, and margins.
Make data accessible for self-service analysis, reporting, and AI-assisted querying through clear structure and documentation.
Work with engineering teams to improve source data quality and resolve issues from ingestion through downstream consumption.
Make practical decisions about architecture and tooling, balancing immediate business needs with maintainability.