Senior Staff Data Engineer - Metrics Platform Lead

Leadout Capital; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Slack is seeking a Staff Data Engineer to set the metrics platform direction, govern data quality, and guide AI-driven analytics. You will own data modeling, semantic layers, and scalable pipelines, mentoring engineers across teams while delivering measurable impact.

In this senior individual contributor role, you will partner with Product, Data Science, Analytics, and Engineering to define metric stewardship, SLAs, and cost-aware architectures that scale with company needs.

Qualifications

• 10+ years of software or data engineering experience
• 6+ years hands-on with data architecture, data modeling, and metadata management
• Led multi-team technical initiatives as an IC
• Deep SQL expertise and Python skills; working knowledge of Go/Java/Scala
• Experience with Spark, Trino, Airflow, Iceberg, Snowflake; dimensional modeling

Responsibilities

• Set architecture and roadmap for metrics platform, balancing freshness, accuracy, reliability, and cost
• Design and scale data models and pipelines turning billions of records into trusted datasets with SLA signals
• Evolve semantic layer and build APIs/tools for metric definitions, lineage, and queries
• Lead metric certification, governance, and design reviews; mentor senior engineers using AI-assisted development

Skills

SQL expertise

Python

Go/Java/Scala

Data modeling

Metadata management

Spark

Trino

Airflow

Iceberg

Snowflake

Education

Related technical degree

Tools

dbt

DataHub

Cube

Job description

Slack is seeking a Staff Data Engineer to set the metrics platform direction, govern data quality, and guide AI-driven analytics. You will own data modeling, semantic layers, and scalable pipelines, mentoring engineers across teams while delivering measurable impact.

In this senior individual contributor role, you will partner with Product, Data Science, Analytics, and Engineering to define metric stewardship, SLAs, and cost-aware architectures that scale with company needs.