Data Analytics Lead - SVP - Hybrid

优缺点分析

角色解读

• Progress to Director or Managing Director within Citi's Markets Technology organization.
• Expand influence across broader ICG (Institutional Clients Group) technology initiatives.
• Move into a more strategic role shaping firm-wide data architecture and analytics strategy.
• Lead a small team of data analysts and developers to build and maintain a data analytics platform for Prime Services.
• Write production-quality Python and Java code for data pipelines, batch jobs, and analytics tools.
• Collaborate with quantitative analysts and business stakeholders to gather requirements and deliver actionable insights from financial data.
• Oversee global production support, ensuring operational stability during Asia morning hours and coordinating with US/UK teams.
• Deep expertise in Python and Java for building data-intensive applications.
• Strong knowledge of financial markets data, including trade lifecycle and Prime Brokerage.
• Experience with distributed systems (Kafka, Redis), SQL/NoSQL databases, and batch processing frameworks.
• Proven ability to manage technical teams while remaining hands-on in engineering.

申请策略

• Tailor your resume to highlight both technical depth and business acumen, especially with quantitative analysts.
• Prepare to discuss how you've balanced hands-on coding with team leadership in past roles.
• Emphasize 12+ years of data engineering experience in financial services, specifically with trading data.
• Showcase leadership roles where you managed small teams while remaining an individual contributor.
• Highlight projects using Python and Java for building production data pipelines and analytics platforms.
• Quantify impact: e.g., 'Reduced batch processing time by 30%' or 'Improved data quality to 99.9%'.
• If not already, deepen knowledge of Prime Brokerage and trade lifecycle data structures.
• Brush up on distributed systems like Kafka and in-memory caches (Gemfire/Redis).

面试指南

• Use the STAR method (Situation, Task, Action, Result) for behavioral questions to provide concrete examples.
• For technical questions, outline your approach: understanding requirements, designing architecture, implementing, testing, and monitoring.
• Show thought process for production issues: identify, isolate, temporary fix, root cause analysis, permanent solution.
• Describe a time you designed a data pipeline for trade data. What technologies did you use and how did you ensure data quality?
• How do you manage a team while still contributing individually to critical engineering tasks?
• Explain how you would gather requirements from quantitative analysts and translate them into a technical solution.
• Walk us through how you would troubleshoot a batch job failure that occurs during Asia morning hours.
• How do you stay up-to-date with evolving data technologies and ensure your team adopts best practices?