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