Data Operations Analyst

As a Data Operations Analyst at Cubist, your primary responsibilities will include providing Level 1 support for mission-critical data pipelines and ensuring daily operations run efficiently. You will handle data validation and entry tasks, conduct security audits, and perform essential incident management. Additionally, you will develop tools and documentation to support ongoing operations and manage complex permission operations across multiple systems.

To qualify for the Data Operations Analyst role at Cubist, candidates should possess at least a Bachelor’s degree in Computer Science or a related discipline. Additionally, a minimum of 3 years of experience in a client-facing support or operations environment is preferred. Familiarity with both Windows and Linux environments, experience in AWS, and a solid understanding of SQL Server and Python are also critical. While experience in the financial industry is preferred, it’s not a strict requirement.

Successful Data Operations Analysts at Cubist must exhibit strong attention to detail and an affinity for processes. Excellent organizational, communication, and interpersonal skills are crucial as you will handle user communication and collaborate with team members. A commitment to ethical standards is also paramount in navigating sensitive financial data.

At Cubist, the work environment for a Data Operations Analyst is collaborative and fast-paced, situated in New York City. The role offers the opportunity to work within a global team that values operational excellence. Analysts benefit from engaging with various financial instruments in a dynamic setting that fosters skill development and encourages innovation.

The Data Operations Analyst plays a crucial role at Cubist by ensuring that data operations run without hiccups. With a focus on delivering error-free, comprehensive data, the analyst supports Systematic Portfolio Managers in making well-informed trading decisions. By managing data pipelines and optimizing daily operations, you will directly contribute to the firm’s efficiency and success in navigating global capital markets.

In response to this question, it's essential to highlight specific examples from your past roles where you successfully validated data. Talk about the methods and tools you used, how you ensured accuracy, and the impact of your validation process on the projects or data sets.

When answering this question, share your approach to incident management, emphasizing your communication strategy with users and management. Discuss any specific methodologies you followed, and highlight successful resolutions you implemented that minimized downtime.

Mention any specific tools or programming languages you're proficient in for data cleaning tasks, like Python libraries (e.g., Pandas) or SQL queries. Share examples of challenges you faced during data cleaning and how you overcame them to ensure high-quality data.

When discussing a challenging data operation, choose an example that showcases your problem-solving skills. Detail the complexity of the situation, your strategic response, and the outcome that resulted from your efforts.

In response to this question, discuss your prioritization strategy. Consider mentioning any tools or techniques you use, such as task matrices or agile methodologies, and illustrate your approach with an example from your experience.

Be prepared to share specific projects where you utilized SQL Server. Discuss your familiarity with writing queries, managing databases, and any performance tuning you've undertaken to improve data retrieval times.

To answer this question, outline a complex permission or entitlement operation you've managed, detailing the systems involved and the protocols you followed. Highlight the importance of these operations to maintain data security and integrity.

Discuss your understanding of data security standards and how you implement them in your daily work. Mention any relevant regulations you’ve worked with and techniques you use to ensure compliance while handling sensitive data.

Make sure to emphasize your experience with both environments, sharing specific examples of tasks you performed in each. Discuss any differences you found in managing data and operations between the two and how you adapted your skills accordingly.

When answering, reflect on your passion for data and operational efficiency. Discuss how you enjoy problem-solving and the opportunity to support the finance industry, showing enthusiasm for making a meaningful impact through your work.