Senior Python Production Support Professional

Position Overview:

The Production Support Consultant is a critical role centered on maintaining the robustness and efficiency of our data processing environment. This position requires a hands-on approach to triaging production issues, diving deep into data problems, and refining/optimizing our data pipelines. This is a production support / Devops role which will involve active collaboration with our investment teams, data engineers and data vendors.

Responsibilities:

• Serve as the first line of defense for production issues, swiftly triaging and prioritizing incidents to ensure minimal disruption to business operations.
• Liaise with external technology vendors and exchanges to coordinate changes and resolve connectivity and market data issues.
• Collaborate with the data engineering team to streamline and optimize data pipelines, ensuring efficient data flow and quality across the organization.
• Configure and optimize production vendor data sourcing jobs, ensuring they run reliably and efficiently, and address any scheduling conflicts or failures promptly.
• Provide timely support to investment researchers for data-related queries and challenges.
• Engage closely with internal teams on system upgrades, data implementations, and incident post-mortems to prevent future issues.
• Review and refine current operational processes, introducing tools or methods that can reduce incident recurrence and improve response times.
• Ability to work in a fast-paced environment and be able to work on multiple projects simultaneously.

Requirements:

• A degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent experience.
• Experience (7+ years) in triaging and resolving production-level issues in a timely manner.
• Proficiency in Python, especially in the context of data operations and pipeline optimizations.
• Practical experience in SQL querying, with the ability to efficiently diagnose and rectify data anomalies.
• Strong problem-solving skills, with an aptitude for diving deep into complex technical challenges.
• Familiarity with enterprise technology tools: Linux, SQL Server, GIT.
• Finance/market data experience: familiarity with vendors such as Bloomberg, Refinitiv, Nasdaq and others is a must.
• Excellent communication skills, both written and verbal, with the ability to articulate technical issues to non-technical stakeholders.
• Ability to work during New York daytime hours or New York evening/overnight hours

Nice to Haves:

• Hands-on knowledge of Tidal Enterprise Scheduler, including configuring, optimizing, and troubleshooting.
• Experience in Python-based data engineering, particularly in optimizing data pipelines and integrations.
• Understanding of cloud fundamentals and best practices in cloud storage, permissions and cost management.