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 provides unified payments and financial infrastructure for businesses worldwide. Its platform combines proprietary infrastructure and software to support business accounts, payments, spend management, treasury, and embedded finance at global scale.
The organization operates across multiple international offices and focuses on building innovative financial technology solutions for businesses around the world. Its culture emphasizes ownership, collaboration, curiosity, sound judgment, rapid execution, and the practical use of AI to solve complex problems.
The Senior Data Engineer will contribute to the development of scalable data infrastructure and solutions that support business intelligence, machine learning, and operational requirements. The role will focus on data modeling, ETL and data pipeline management, data governance, and the intersection of data engineering and AI.
Key Responsibilities
Data Modeling
- Design and implement robust, scalable data models supporting business intelligence, machine learning, and operational use cases.
- Apply a strong understanding of data schemas and select appropriate designs, including star schema, snowflake, normalized, and denormalized models based on business requirements.
- Collaborate with business teams to translate data requirements into clean, structured, and well-documented data models.
- Promote the concept of Single Source of Truth (SSOT) throughout data layers and pipelines.
- Maintain data consistency, traceability, and quality across multiple data sources and business domains.
ETL and Data Pipeline Management
- Build and maintain both batch and streaming ETL pipelines.
- Manage end-to-end data workflows, from data ingestion and transformation through to data delivery.
- Collaborate closely with Data Platform Engineers and Product Managers to identify the root causes of data issues.
- Develop efficient and scalable solutions to address data pipeline and infrastructure challenges.
- Contribute to data migration, duplication, and consistency initiatives across distributed or multi-datacenter environments.
Data Governance
- Participate in the development and implementation of data governance strategies, policies, and standards.
- Contribute to data quality and data stewardship initiatives.
- Support metadata management and master data management practices.
- Help address data privacy, security, and data lifecycle requirements.
- Promote consistent governance practices across data platforms and domains.
Data and AI
- Develop a foundational understanding of artificial intelligence and its applications within data engineering.
- Explore practical and innovative ways to integrate data engineering capabilities with AI.
- Support initiatives that leverage data to improve AI-driven solutions and business outcomes.
Required Qualifications
- Strong experience in data engineering, particularly in data modeling or ETL and data pipeline development.
- Strong understanding of data schemas and data modeling methodologies.
- Experience building and maintaining batch and streaming data pipelines.
- Understanding of end-to-end data workflows, including data ingestion, transformation, and delivery.
- Ability to collaborate effectively with Data Platform Engineers, Product Managers, business teams, and other stakeholders.
- Experience identifying and resolving complex data issues.
- Understanding of data quality, consistency, traceability, and governance principles.
- Familiarity with data governance concepts and practices.
- Basic understanding of AI and an interest in applying AI to data engineering.
- Strong problem-solving, analytical, communication, and collaboration skills.
Preferred Qualifications
- Experience working with distributed or multi-datacenter data systems.
- Experience addressing data migration, duplication, and consistency challenges.
- Experience across multiple areas of data engineering, including data modeling, ETL, data governance, and Data + AI.
- Experience supporting business intelligence and machine learning use cases.
- Ability to work effectively in a fast-paced environment with complex, high-impact technical challenges.
- Curiosity and willingness to learn new technologies and approaches.
- Ability to use AI tools and technologies to improve productivity and solve problems efficiently.
Disclaimer
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