Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global financial technology company is building a unified payments and financial platform designed to help businesses manage accounts, payments, spend management, treasury, and embedded finance at scale. With a global team of technology professionals across multiple offices, the organization focuses on developing innovative financial infrastructure and software for businesses worldwide.
The organization values builders with strong ownership, curiosity, sound judgment, and a collaborative mindset. Team members are encouraged to move quickly while maintaining engineering rigor, use AI to improve productivity, and take ownership of complex, high-impact challenges.
Team Leadership & People Management
- Hire, coach, and develop a team of data engineers while establishing clear expectations and providing regular feedback and career development support.
- Establish team priorities, rituals, and working practices that balance delivery speed with engineering rigor.
- Serve as a technical mentor by reviewing designs and code where appropriate and helping engineers strengthen their skills in data modeling, pipeline engineering, and governance.
- Manage team performance, workload, and hiring plans according to business requirements.
- Drive AI strategy and AI automation initiatives within the team.
Data Modeling Strategy
- Set the technical direction for data modeling across the team.
- Select appropriate schema designs, including star schema, snowflake, normalized, and denormalized approaches, based on business requirements.
- Champion the concept of a Single Source of Truth (SSOT) across data layers and pipelines.
- Ensure data requirements are translated into clean, structured, and well-documented models in collaboration with business stakeholders.
- Oversee data consistency, traceability, and quality standards across multiple data sources and domains.
ETL & Data Pipeline Oversight
- Guide the development and maintenance of batch and streaming ETL pipelines from data ingestion through transformation and delivery.
- Strengthen collaboration between data engineering teams, Data Platform Engineers, and Product Managers to resolve data issues quickly and implement durable solutions.
- Provide technical judgment on distributed and multi-datacenter challenges, including data migration, duplication, and consistency.
- Ensure data pipelines are reliable, scalable, maintainable, and aligned with business requirements.
Data Governance
- Own and continuously evolve data governance strategies, policies, and standards across assigned domains.
- Ensure engineering practices address key data governance areas, including data quality, data stewardship, metadata management, master data management, data privacy and security, and data lifecycle management.
- Represent the data engineering team in cross-functional data governance discussions and decision-making processes.
- Promote consistent governance practices across data platforms and business domains.
Data + AI
- Drive practical and high-impact opportunities for combining data engineering and AI capabilities.
- Help establish the data foundations required to support AI-powered applications and workflows.
- Identify opportunities to use AI and automation to improve engineering productivity and operational efficiency.
- Encourage the team to adopt emerging AI technologies where they can create meaningful business and engineering value.
Required Qualifications
- Experience leading and managing data engineering teams.
- Strong experience with data modeling, data pipelines, and analytics-ready datasets.
- Experience designing and implementing batch and streaming ETL pipelines.
- Strong understanding of data governance, data quality, metadata, data stewardship, privacy, and security.
- Experience working with distributed data systems and complex data architectures.
- Strong technical understanding of data engineering principles and best practices.
- Experience collaborating with engineering, product, and business stakeholders.
- Strong leadership, mentoring, communication, and organizational skills.
- Ability to translate ambiguous business requirements into scalable technical solutions.
- Experience applying AI or automation to data engineering workflows is highly valued.
Location: San Francisco, with a hybrid working model.
Disclaimer
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- No Client Relationship: This company is not a client of SpringCube unless stated.
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