Data Analytical Engineer, Payment Data Intelligence - USDS

Position Overview

In the role of Data Analytical Engineer, Payment Data Intelligence - USDS, you will design, build, and maintain scalable data infrastructure and ETL/ELT pipelines to transform complex payment data into reliable and actionable datasets. You will work within the Global Payment team of U.S. Data Security to ensure a smooth and secure payment experience on the TikTok platform. Location: San Jose.

Key Responsibilities

• Design and Development: Design, build, and optimize robust, scalable data models and ETL/ELT pipelines to ingest, process, and transform massive volumes of payment transaction data from various sources.
• Infrastructure Management: Manage and maintain data warehouse environments and data lake infrastructure, ensuring high performance, availability, and security.
• Data Quality and Governance: Implement automated data quality checks, monitoring, and validation processes to ensure accuracy, completeness, and consistency of datasets.
• Performance Optimization: Tune data pipelines and queries for maximum performance and cost efficiency, particularly for real-time data flows.
• Collaboration: Work closely with Data Scientists, Data Analysts, Product Managers, and other teams to understand data requirements and deliver custom solutions for reports, dashboards, and analytical models.
• Tooling and Technology: Evaluate and implement new technologies and tools to improve data engineering efficiency and the overall data ecosystem.
• Documentation: Create and maintain comprehensive documentation for data models, architecture, and ETL/ELT processes.

Required Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.
• Experience Required: 3+ years of professional experience in Data Engineering, Analytical Engineering, BI Engineering, or a similar role.
• SQL Expertise: Expert-level knowledge of SQL with deep experience in managing large, complex datasets in a cloud-based data warehouse.
• Programming: Strong proficiency in a programming language such as Python used for data manipulation and pipeline construction.
• Data Pipelining: Hands-on experience designing and building production-grade data pipelines using orchestration tools.
• Data Modeling: Solid understanding of data warehousing concepts, dimensional modeling (Star/Snowflake schema), and OLAP structures.
• Cloud Platforms: Experience with at least one major cloud platform and its associated data services.

Preferred Qualifications

• Domain Knowledge: Previous experience working with payment data (e.g., card transactions, ACH, wallets), financial data, or fraud/risk datasets.
• Streaming Experience: Experience with real-time or streaming data technologies such as Kafka, Kinesis, or Spark Streaming.
• Data Ops: Experience with data SLA monitoring, data storage, and computing resource management and optimization.

Benefits & Perks

• Compensation: The base salary range for this position in the selected city is $118657 - $259200 annually.
• Benefits: Employees have day one access to medical, dental, and vision insurance; a 401(k) savings plan with company match; paid parental leave; short-term and long-term disability coverage; life insurance; wellbeing benefits; 10 paid holidays per year; 10 paid sick days per year; and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).