Data Engineer

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 leading global financial technology company provides an intelligent finance platform that helps businesses manage spending, payments, banking, corporate cards, and travel. Its AI-powered automation and financial management solutions help companies improve operational efficiency, gain real-time visibility, and maintain greater control over their spending.

The organization supports thousands of companies across global markets and promotes a collaborative, inclusive environment where employees are encouraged to challenge conventional approaches, develop their skills, and grow their careers.

Data at the Organization

The Data team works closely with Scientists and Engineers to make data and data-driven insights a core asset across the organization. The team develops data infrastructure, statistical models, and data products that support business decision-making, operational efficiency, risk management, and customer experiences.

The organization is seeking a Data Engineer to transform raw data into actionable insights for teams across the business. This role will collaborate closely with Data Scientists, Software Engineers, Data Analysts, and business stakeholders to develop efficient data models, pipelines, and analytics frameworks. The Data Engineer will also play a leading role in designing, implementing, and maintaining high-quality core data tables that serve as trusted sources for analytical applications.

Work Arrangement

This role is based in the organization’s San Francisco office and follows a hybrid working model. Employees are currently expected to work in the office a minimum of three coordinated days per week, Monday, Wednesday, and Thursday. The role also provides the opportunity for up to four weeks of fully remote work per year.

Key Responsibilities

• Design, build, and maintain scalable data models and pipelines that support the organization’s growing number of services, products, and evolving business requirements.
• Collaborate with Data Scientists, Data Analysts, and business teams to understand data requirements and translate them into reliable, efficient, and scalable data solutions.
• Enable predictive analytics, data analysis, and metrics development through well-designed data infrastructure.
• Maintain data documentation, definitions, and source-of-truth tables to ensure high-quality data for data science and reporting applications.
• Develop and maintain integrations with a variety of data sources to support data-driven initiatives across the organization.
• Apply data management best practices to ensure the reliability, consistency, and robustness of data used across analytics applications.
• Establish and promote organization-wide standards for data structure, quality, and expectations.
• Act as a liaison between technical and non-technical stakeholders, ensuring data solutions align with business objectives.
• Use agentic AI where appropriate to accelerate data pipeline development and quality-related work while maintaining proper validation processes.

Required Qualifications

• 3+ years of experience in Data Engineering, Data Analytics, Analytics Engineering, or a related field.
• Advanced knowledge of databases and SQL, including the ability to efficiently stage, process, and transform data.
• Experience integrating and orchestrating data workflows using modern data tools and systems.
• Experience with data modeling, ETL/ELT processes, and data warehousing solutions.
• Experience working with a cloud data warehouse such as Snowflake.
• Experience using a data workflow orchestration platform such as Airflow.
• Experience with a programming language such as Python.
• Experience working with agentic AI.
• Exceptional quantitative and analytical skills.
• Strong communication skills and the ability to collaborate effectively with technical and non-technical stakeholders.

Preferred Qualifications

• Familiarity with business intelligence tools such as Looker, Tableau, or similar platforms.
• Strong production experience with dbt, including models, tests, documentation, and collaboration within shared repositories.
• Experience developing AI-ready data structures where metrics and dimensions are clearly defined and reusable for AI-assisted analysis.
• Experience integrating Salesforce data.
• Understanding of modern data engineering and analytics practices.
• Experience establishing and maintaining data quality standards across an organization.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
3. To Apply: Click the Apply button to be redirected to the hiring company’s application page for this job.
4. No Liability: SpringCube is not liable for inaccuracies.