Staff Data Engineer: Architect of Enterprise-Scale Analytics

United States Digital Space LLC; San Francisco (CA); On-site; USD 279,000 - 310,000; Full time

Job summary

United States Digital Space LLC is seeking a Staff Data Engineer to lead the analytics data infrastructure and governance efforts. You will drive technical vision for curated datasets and ensure data quality, auditability, and self-service access across teams.

You will partner with Data Governance, Data Science, Product, Finance, and Engineering to implement robust data models, governance, and scalable data pipelines.

Qualifications

• 7+ years of experience in data and software engineering focusing on curated datasets
• Experience designing and maintaining data infrastructure used by large data teams
• Expert SQL and strong Python skills with version control/CI/CD
• Proven track record implementing data quality, monitoring, and remediation at scale
• Strong business acumen and ability to translate questions into metric definitions for stakeholders
• Collaborative with Data Science, Finance and Product teams

Responsibilities

• Define strategy and architecture for analytics data infrastructure and curated datasets
• Lead data governance programs end-to-end with stakeholders
• Design and build data abstractions and analytical frameworks using SQL and Python
• Develop data quality, monitoring, anomaly detection, and governance at scale
• Promote consistent data modeling, naming conventions, and metric governance
• Mentor engineers and lead cross-functional data initiatives

Skills

7+ years data engineering

SQL

Python

data governance

CI/CD

communication

team leadership

Tools

dbt

BigQuery

Job description

United States Digital Space LLC is seeking a Staff Data Engineer to lead the analytics data infrastructure and governance efforts. You will drive technical vision for curated datasets and ensure data quality, auditability, and self-service access across teams.

You will partner with Data Governance, Data Science, Product, Finance, and Engineering to implement robust data models, governance, and scalable data pipelines.