Operations Analyst (Data & Automation)

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.
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.