Head of Data Platform & Analytics

Galileo Financial Technologies, LLC; San Francisco (CA); On-site; USD 190,000 - 260,000; Full time

Job summary

SoFi Tech Solutions in San Francisco seeks a Senior Engineering Manager to lead the Data Team, owning the roadmap for data collection, modeling, governance, and serving. You will build the team needed to deliver on these initiatives in a regulated fintech context.

This role requires experience scaling data organizations, shaping architecture, and driving a strong team culture while collaborating with Finance, Risk, Product, and Operations to translate analytics needs into scalable data models.

Qualifications

• 8+ years in data engineering, infrastructure, or related field.
• 5+ years directly managing engineers or analysts.

Responsibilities

• Own hiring, onboarding, and career development for Data Platform and Data Analytics teams.
• Set technical direction and engineering standards for data pipelines, warehousing, and analytics tooling.
• Manage team roadmap, prioritization, and delivery against business goals.
• Own architecture and reliability of our data platform (ingestion, ETL/ELT, orchestration, storage, and serving layers).
• Extend data governance and compliant handling practices leveraging the enterprise Data Platform.
• Partner with Finance, Risk, Product, and Operations to translate analytics needs into scalable data models.
• Oversee dashboards and semantic models, ensuring data quality and a single source of truth.

Skills

Leadership experience

Data-driven decisions

Strategic thinking

Stakeholder management

Tools

Airflow

dbt

Snowflake

Kafka

Terraform

AWS

Job description

SoFi Tech Solutions in San Francisco seeks a Senior Engineering Manager to lead the Data Team, owning the roadmap for data collection, modeling, governance, and serving. You will build the team needed to deliver on these initiatives in a regulated fintech context.

This role requires experience scaling data organizations, shaping architecture, and driving a strong team culture while collaborating with Finance, Risk, Product, and Operations to translate analytics needs into scalable data models.