Job Description
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Company Overview
A leading global financial technology company provides a unified payments and financial platform that enables businesses worldwide to manage accounts, payments, spend management, treasury, and embedded finance solutions at scale. With a global workforce spanning multiple international offices, the organization is focused on building the future of global banking through innovative technology, scalable infrastructure, and customer-centric financial products.
The organization is seeking a Senior Data Engineer, Knowledge Platform Engineering to join its data team in San Francisco. This role focuses on designing scalable data solutions, developing robust ETL pipelines, contributing to data governance initiatives, and exploring practical applications of AI within data engineering.
The ideal candidate will possess strong expertise in data modeling and ETL processes while collaborating closely with engineering, product, and business stakeholders to build high-quality, scalable, and reliable data platforms. This position offers the opportunity to solve complex, high-visibility challenges in a fast-paced and collaborative environment.
Key Responsibilities
- Design and implement robust and scalable data models to support business intelligence, machine learning, and operational requirements.
- Develop and maintain clean, structured, and well-documented data models across multiple business domains.
- Apply appropriate schema designs, including star schema, snowflake schema, normalized, and denormalized models, based on business use cases.
- Promote and maintain Single Source of Truth (SSOT) principles across data layers and pipelines.
- Ensure data consistency, traceability, and quality across diverse data sources.
- Build and maintain batch and streaming ETL pipelines from ingestion through transformation and delivery.
- Collaborate with Data Platform Engineers and Product Managers to identify root causes of data issues and implement scalable solutions.
- Support distributed and multi-datacenter data environments, including data migration, duplication, and consistency initiatives.
- Contribute to data governance strategies, policies, and standards across the organization.
- Support key data governance pillars, including data quality, metadata management, data stewardship, master data management, privacy, security, and lifecycle management.
- Explore and contribute to practical applications of AI within data engineering workflows and processes.
Required Qualifications
- Bachelor’s degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field.
- Minimum of 5 years of experience designing and implementing ETL pipelines using tools such as Informatica, Talend, Apache NiFi, or similar platforms.
- Strong proficiency in SQL and experience with database management systems, including MySQL, PostgreSQL, and Oracle.
- Experience with data warehousing technologies and cloud platforms, particularly Google Cloud Platform (GCP), BigQuery, and Airflow.
- Strong problem-solving skills with a high level of attention to detail and commitment to quality.
- Excellent communication and collaboration skills with the ability to thrive in fast-paced, team-oriented environments.
- Outstanding verbal communication skills and experience working with globally distributed teams.
Preferred Qualifications
- Experience working within financial services, payments, or fintech organizations.
- Familiarity with data governance practices and regulatory requirements within the financial industry.
- Experience with scripting languages such as Python or R for data analysis and automation.
- Relevant certifications in data management or related technologies.
- Experience across multiple areas of data engineering, including data modeling, ETL, data governance, and AI applications.
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