Senior Data Engineer - Data Platform

Senior Data Engineer (Data Platform) (StubHub, Inc., New York, NY)

Drive architectural decisions and establish standards for scalable data infrastructure, pipeline frameworks, and data management practices. Influence the direction for key infrastructure and framework choices for data pipelining and data management. Build and deploy core cross-functional data models. Manage individual initiatives setting priorities, deadlines, and deliverables based on technical expertise. Mentor team members and help foster a culture of innovation, accountability, and engineering excellence. Utilize advanced SQL proficiency to work with both OLAP/OLTP databases and apply knowledge of one or more programming languages such as Python or Java. Design and develop large-scale, efficient batch and streaming data processing workflows. Analyze and reason about large datasets to identify data quality issues and extract contextual insights. Design, build, and maintain scalable ingestion pipelines for financial and transactional data from third-party payment processors, banking partners, and external APIs while ensuring data quality, reliability, and compliance with security and governance standards. Collaborate with non-technical stakeholders to understand, anticipate, and deliver on their data needs. Work with orchestrator tools, such as Airflow or Dagster along with data transformation frameworks like dbt or SQLMesh. Telecommuting may be permitted up to 2 days per week. When not telecommuting, must report to StubHub, Inc. at 175 Greenwich Street, Floor 59, New York, NY 10007. Salary: $225,000 to $250,000 per year.

MINIMUM REQUIREMENTS: Bachelor’s degree or U.S. equivalent in Mathematics, Quantitative Economics, Statistics, Computer Science, or a related field, plus 6 years of professional experience as a Data Scientist, Data Engineer, or any occupation, job title, position integrating, processing, and reconciling transactional data.

Must also have experience in the following: 6 years of professional experience developing scalable data pipelines with robust quality assurance (QA), monitoring, and alerting mechanisms; 6 years of professional experience analyzing and reasoning about large datasets to identify data quality issues, troubleshoot anomalies, and generate actionable insights; 5 years of professional experience performing advanced SQL programming across OLAP and OLTP databases including designing, optimizing, and troubleshooting complex queries and data models; 4 years of professional experience utilizing programming languages including Python to build scalable data pipelines, processing frameworks, and data-driven applications; 4 years of professional experience utilizing modern data stack technologies and cloud data platforms (including Snowflake); 4 years of professional experience working with data orchestration tools (including Airflow) and data transformation frameworks (including dbt); 4 years of professional experience designing, building, and operating large-scale, efficient batch and streaming data processing systems; and 1 year of professional experience working with ERP (Enterprise Resource Planning) systems including NetSuite and Oracle ERP.

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