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# Data Analytical Engineer, Payment Data Intelligence - USDS

- **Company:** Tiktok Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $122,574.0 - $259,200.0
- **Contract:** Permanent contract
- **Skills:** Adaptable Database Systems, Big Data, Cloud Computing, Cloud Database, Computer Programming, Data as a Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, Dimensional Modeling, Payment Systems, Fraud Prevention and Detection, Data Intelligence, Python (Programming Language), Online Analytical Processing, Performance Tuning, Standard Sql, Data Streaming, Data Processing, Data Storage Technologies, Cloud Platform System, Data Lakes, Information Technology, Data Analytics, Star Schema, Real Time Data, Apache Kafka, Spark Streaming, Data Pipelines, Programming Languages
- **Published:** June 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a567c2b339c20c2f

## About the Role

Do you have experience in Schema design?, Minimum Qualifications: - Education: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field. - Experience: 3+ years of professional experience in Data Engineering, Analytical Engineering, BI Engineering, or similar role. - SQL Expertise: Expert-level knowledge of SQL and deep experience working with large, complex datasets in a cloud-based data warehouse. - Programming: Strong proficiency in a programming language used for data manipulation and pipeline construction (e.g., Python). - 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 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 (e.g., Kafka, Kinesis, Spark Streaming). - Data Ops: Data SLA monitoring and assurance, data storage and computing resource management and optimization.

## Description

About the Team: The Global Payment team of U.S. Data Security department of TikTok provides payment solutions - including payment acquisitions, disbursements, transaction monitoring, payment method management, foreign exchange conversion, accounting, reconciliations, and so on - to ensure that users have a smooth and secure payment experience on TikTok platform. The Payment Data Intelligence team is responsible for providing data driven intelligence to empower payment business growth, optimize payment processes and improve customer experience. We're looking for a highly skilled and motivated Data Analytical Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining the scalable data infrastructure and ETL/ELT pipelines necessary to transform complex raw payment data into reliable, high-quality, and actionable datasets. This role is critical in driving strategic decision-making and optimizing our payment operations, fraud detection, and
financial reporting. 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 data sources (RDS, MQ, 3rd Party etc). - Infrastructure Management: Manage and maintain our data warehouse environment 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 the accuracy, completeness, and consistency of all payment intelligence datasets. - Performance Optimization: Tune data pipelines and queries for maximum performance and cost efficiency, especially for real-time and near real-time data flows. - Collaboration: Work closely with Data Scientists, Data Analysts, Product Managers, and solution teams to understand their data requirements and deliver custom data solutions that power 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.


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