Operations Analyst (Data & Automation)

Responsibilities

The Operations Analyst (Data & Automation) is the Logistics team's in-house builder of data tools and automation. They turn business needs into small, reliable systems: data transformation (ETL) flows, dashboards and reports, data-entry tools for scattered operational data, and AI-assisted workflows. They also support the Business Analyst with requirement gathering, SOP documentation and reporting, and work closely with the Logistics team to drive improvement.

• Build and maintain data transformation (ETL) flows that produce deterministic, verifiable output, reconciled against source data.
• Build internal web tools to capture operational data that is not available via API, with input validation and audit trails.
• Design and maintain database schemas for data owned by these tools, using constraints and validation to keep data clean (no duplicates, no orphaned records).
• Build dashboards and reports on top of the existing data platform.
• Build AI-assisted workflows where they fit, with checks on the AI's output.
• Write tests from known-input and expected-output cases agreed with the requester.
• Reuse and extend existing live tools as templates, and document each tool shipped (purpose, inputs and outputs, how to deploy, how to test).
• Use AI coding tools in daily work and be able to explain any code shipped.
• Work with Logistics team members to understand their daily processes, collect feedback after each launch, and improve tools based on how they are used.
• Support requirement gathering, SOP documentation, and ad hoc reporting for the logistics team.
  
  

Requirements

Education

• Bachelor's degree in Computer Science, Information Systems, Data or related field.
  
  

Experience

• 0–2 years of experience
• Fresh graduates are welcome if they have relevant internship, freelance, or part-time experience.
• Has built at least one project end-to-end that other people used (internship, work or personal).
• Experience with AI coding assistants, and the judgment to check their output.
  
  

Skills

• Python and SQL: able to read, write and debug both independently.
• Relational database fundamentals: table design, primary and foreign keys, constraints, joins, indexes and transactions; hands-on experience with a relational database such as PostgreSQL, MySQL or SQL Server.
• Working knowledge of Git and basic automated testing.
  
  

Attributes

• Clear written communication, and comfortable taking requirements from non-technical colleagues.
• Genuine interest in business operations (e.g., logistics or supply chain) and willingness to learn how the logistics team works.
• Able to work from the office when required.