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Data Operations Analyst

Two Sigma
🇯🇵TokyoJuniorFull-timePosted October 9, 2026

About the Role

Two Sigma is seeking a talented Data Operations Analyst to join our Global Data Operations team in Tokyo. GDO is a service provider for data-driven operations and operational risk management: we create, maintain, and operate the optimized workflows that provide and curate the data used in live trading. You'll be part of a close-knit, global team whose daily execution is essential to the success of Two Sigma's business.

This is a hands-on, technical operating role. We are looking for someone who is comfortable at the level of the database, the shell, the job log, and the file system, who can pull apart a problem with a diverse set of tools, and who wants to make our operations measurably better by writing and improving the runbooks we run every day.

  • Investigate and remediate production alerts using incident-specific runbooks, owning each issue through to resolution or escalation
  • Author and improve runbooks, applying existing data-management workflow standards, turning recurring, ad-hoc fixes into clear, unambiguous, repeatable procedures that lower operational risk and increase throughput
  • Mine our data to surface patterns and help root-cause issues, validate data, and reveal trends across large volumes
  • Apply predefined acceptance and quality criteria to guarantee the integrity of financial data sets
  • Collaborate with business partners, software engineers, data analysts, and other data operations specialists to understand and improve business processes
  • Recommend and improve data-management policies and procedures to improve efficiency and accuracy

This is a Tokyo-hours operational role within a global team that supports trading 24/5 across Houston, Tokyo, and London. Support is interrupt-driven and time-sensitive.

Responsibilities

  • Investigate and remediate production alerts using incident-specific runbooks, owning each issue through to resolution or escalation
  • Author and improve runbooks, applying existing data-management workflow standards, turning recurring, ad-hoc fixes into clear, unambiguous, repeatable procedures that lower operational risk and increase throughput
  • Mine our data to surface patterns and help root-cause issues, validate data, and reveal trends across large volumes
  • Apply predefined acceptance and quality criteria to guarantee the integrity of financial data sets
  • Collaborate with business partners, software engineers, data analysts, and other data operations specialists to understand and improve business processes
  • Recommend and improve data-management policies and procedures to improve efficiency and accuracy

Requirements

  • Comfort with 'low-level' technology - can work behind the UI: the UNIX/Linux command line, querying databases (SQL) to mine and validate data, reading logs, and navigating file systems, schedulers, and storage
  • Exposure to scripting – Bash, Python, or a similar language – and the judgment to know when a script helps and when a runbook is the right answer
  • A genuine pull toward operations: you like to execute daily, at volume, follow workflows to the letter, and feel the immediate impact of work done right
  • Comfort switching between systems and tools – Linux/Windows, database/file-server, UI/command-line, text/numbers – without being dependent on a single framework
  • Strong attention to detail and exceptional verbal and written communication
  • BS in a technical discipline or equivalent hands-on experience in a comparable operational or technical role
  • Minimum 1 year of experience required; 1-5 years of experience preferred
  • Professional experience in data operations, technical operations, or a similar discipline – e.g. data operations analyst, systems/support engineer, data analyst, process engineer, systems administration, QA, or a structured operational background such as defense analysis, logistics, or maintenance technician work where you followed and built step-by-step procedures and worked with data and tooling
  • Hands-on experience performing data-management and data-quality tasks in data-intensive environments
  • A desire to grow your knowledge of finance and trading