Staff Data Engineer

Galileo Financial Technologies is a financial technology company that provides innovative software products and services for Fintechs. They are seeking a Staff Data Engineer to shape the architecture of their cloud-native data platform, establish technical direction, and drive complex cross-team initiatives to enhance performance and reliability.

Responsibilities:

• Shape the architecture of Galileo’s cloud-native, multi-tenant data platform, defining patterns and workflows used by internal teams and external clients
• Design domain data models, contracts, and semantic layers that improve clarity, trust, and consistent analytical and operational use across the business
• Build and evolve reusable platform components that raise engineering standards and eliminate one-off, pipeline-by-pipeline work
• Lead strategy for automated data quality, lineage, and observability, moving us toward metadata-driven systems with clear SLOs
• Drive complex cross-team initiatives, creating clarity, aligning stakeholders, and guiding architectural decisions that move the platform forward
• Resolve systemic platform issues through durable improvements that enhance performance, reliability, and developer productivity
• Partner with Product, Platform, Security, and Analytics to align priorities and connect technical decisions to broader organizational goals
• Raise engineering standards through strong design reviews, improved CI/CD practices, and automation that reduces operational load
• We’re looking for someone who not only raises the technical bar, but also strengthens our small, high-trust team through thoughtful communication, ownership, and a genuine commitment to being a culture add

Requirements:

• 8+ years of professional data platform engineering experience, with proven depth leading complex, cross-team initiatives and evolving long-lived systems
• Expert-level SQL and strong proficiency in Python or Java, with a track record of building reliable, maintainable, well-tested data systems
• Deep Snowflake expertise, including architectural design, warehouse optimization, cost/performance trade-offs, data sharing, and advanced platform features
• Strong experience in a major cloud environment (AWS preferred), designing systems that are secure, scalable, observable, and aligned to best practices
• Expertise with workflow orchestration (Airflow preferred) and transformation frameworks (dbt, PySpark, Snowpark)
• Experience shaping BI or semantic layers ensuring durable, consistent definitions across domains
• Strong grounding in Git-based workflows and CI/CD, with the ability to improve engineering practices, reduce operational friction, and raise release reliability across squads
• Demonstrated Staff-level leadership - creating clarity from ambiguity, making sound architectural trade-offs, mentoring across teams, and driving alignment through clear, direct communication
• AWS experience preferred
• Airflow experience preferred