The Junior Data Engineer supports the Finance department's day-to-day data operations within a 3PL business environment. The primary focus of this role is data processing and verification — including logistics billing reconciliation and customer pricing data preparation — while also assisting senior engineer with the gradual build-out of automated workflows. A foundational technique of programming is expected, with the opportunity to develop technical skills on the job. The role also assists on the build-out of the company's BigQuery data room, and is expected to work AI-first — using AI coding assistants and LLM-based tools as the default approach to day-to-day data work.
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Billing Verification & Reconciliation: Process and verify logistics carrier invoices; cross-check billing data against system records and internal references; establish validation rules and flag discrepancies and follow up with relevant parties for resolution.
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Customer Pricing Data Preparation: Assist senior data engineers in collecting, organizing, and maintaining customer rate data; create data quality check rules to support the preparation of customer pricing and perform basic data quality checks to ensure accuracy.
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Automation Support & Learning: Assist senior data engineers in testing and validating automated workflows; complete daily data query and validation, and proactively learn to take on more technical tasks over time.
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Ad-hoc Data Tasks: Handle data processing and reporting requests from the Finance department and cross-functional teams as assigned, including data collation, formatting, and basic transformation tasks.
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BigQuery Data Room Support: Assist the senior data engineer on the BigQuery data room project — loading and staging source data, writing and testing SQL queries and transformation logic, documenting table structures and field definitions, and running data quality checks to confirm that modelled data ties back to the source systems.
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AI-First Ways of Working: Use AI and LLM-based tools as the default approach to daily work — including AI coding assistants for writing and debugging SQL and Python, and LLM tools for data checks, documentation and reconciliation support. Write and refine prompts, build reusable prompt templates and lightweight AI-assisted workflows for recurring tasks, and always validate AI output against source data before it is relied upon.
Requirements
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Bachelor's degree or above in Computer Science, Information Systems, Statistics, or a related field.
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2–3 years of relevant working experience in data processing, operations, or a related role; logistics or 3PL industry background is a plus.
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Working knowledge of SQL and basic Python; hands-on exposure to a cloud data warehouse — Google BigQuery in particular — is a strong advantage, and a genuine willingness to grow technically is essential.
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Comfortable working with large volumes of data manually; proficient in Excel for data collation, cross-referencing, and basic analysis.
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Demonstrated hands-on use of AI tools (e.g. ChatGPT, Claude, GitHub Copilot, Cursor) in data or engineering work; able to write effective prompts, use AI coding assistants to produce and debug SQL and Python, and critically verify AI output against source data. Candidates should be prepared to describe how they currently use AI in their day-to-day work.
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Meticulous attention to detail and a strong sense of data accuracy; able to identify inconsistencies in complex datasets.
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Self-motivated, adaptable, and willing to work in a hands-on, process-building environment where systems are still maturing.
