Stripe9 дней назад
Data Analyst, Financial Data Engineering
Зарплата не указана
New York
Обязанности
- 01Design, build, and maintain scalable data pipelines and ETL/ELT workflows that power production-grade financial reporting, risk measurement, and operational decisioning for Treasury Finance
- 02Leverage AI tools (code assistants, LLM-based agents) to accelerate pipeline development, data quality automation, reconciliation, and documentation - expanding technical scope while maintaining quality
- 03Model and transform raw data into clean, well-documented datasets that serve as the core foundations for decision making for Treasury Finance (e.g. float positions, cash explainability, risk exposures, liquidity management)
- 04Establish and enforce data quality standards through testing, monitoring, and alerting on pipeline health
- 05Establish and own data freshness SLAs, operational alerting, and incident response for your data domains - ensuring production reliability for risk and finance critical workflows
- 06Partner deeply with Treasury Finance, data scientists/analysts, and engineers to define data requirements and deliver trusted, reusable financial data products
- 07Partner deeply with Treasury Finance stakeholders to translate business requirements into data architecture decisions, anticipating needs and helping to drive data strategy rather than reacting to requests
- 08Build self-service tooling and analytics layer that empower stakeholders to access and explore trusted data autonomously
Требования
- 016+ years of full-time experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or a related analytical role
- 02Proficiency in SQL, including complex query optimization and data modeling
- 03Proficiency in Python for data pipeline development, not just scripting
- 04Experience with distributed data frameworks like Spark to write and debug data pipelines
- 05Experience with workflow orchestration tools (e.g. Airflow, Flyte, or equivalent)
- 06Proven ability to design, implement, and maintain production-grade data pipelines and dashboards
- 07Good understanding of development processes and best practices like engineering standards, code reviews, and testing
- 08Ability to clearly communicate results and drive impact with cross-functional partners
- 09Experience owning production data products with defined quality standards, testing, and documentation