Job Title: Junior Data Management Analyst (Excel & Python)

- Location: Sydney, NSW (Hybrid)

Position Purpose

The Junior Data Management Analyst ensures the absolute integrity and cleanliness of our commercial and retail data assets. In this role, you will replace manual workflows with automated data pipelines. By combining advanced Excel modelling with Python scripting, you will ingest, scrub, and structure messy retail datasets into high-performance data models that power the company’s commercial decisions.

1. Key Responsibilities

Data Cleansing & Pipeline Maintenance

- Automate data scrubbing: Write, test, and maintain Python scripts to clean and normalise unstructured, erratic retail scan data (e.g., Woolworths, Coles, Aztec/Nielsen).
- Handle data exceptions: Build automated validation loops to isolate and flag corrupted data formats, missing SKU codes, or broken pricing brackets.
- Maintain code repositories: Ensure all data preparation scripts are cleanly documented, version-controlled, and easily executable by other team members.

Master Data Integrity & Governance

- Maintain the "Single Source of Truth": Audit and reconcile master data files (SKUs, pricing tiers, promotional codes) across internal systems and cloud-based databases.
- Format & map datasets: Use Power Query and Python libraries to reshape disparate vendor file schemas into a uniform, production-ready format.
- Process mapping: Document backend data flows, data dictionaries, and structural cleaning steps to ensure compliance with company data governance policies.

Commercial Reporting Support

- Optimise spreadsheet templates: Build and refine advanced Excel validation tools using energetic formulas to help commercial teams double-check their datasets. [3]
- Feed downstream analytics: Prepare clean, structured data extracts for direct ingestion into executive Power BI dashboards or financial forecasting tools.

2. Technical Skills & Qualifications

Python Capabilities (Core Requirement)





- Data manipulation: Practical proficiency using pandas and NumPy to clean, merge, slice, filter, and aggregate high-volume data tables.
- File handling: Ability to write scripts that read and write across .csv, .xlsx, .json, and text formats, handling file paths dynamically using variables.
- Script design & logic: Clean understanding of core programming mechanics, including lists, dictionaries, try-except error handling, for/while loops, and writing modular, reusable functions.

Advanced Excel Competencies (Core Requirement)

- Power Query integration: Mastery of Power Query GUI and basic M-code to extract, transform, and pivot data without crashing the spreadsheet environment.
- Advanced Formula Engine: Expertise with dynamic array functions (LET, LAMBDA, FILTER, UNIQUE) to build self-updating data reconciliation tables.
- Model hygiene: Knowledge of how to build memory-efficient spreadsheets by avoiding volatile functions on massive datasets.

Education & Experience

- Qualifications: Bachelor’s degree in Business Analytics, Information Systems, Data Science, Computer Science, Mathematics, or a highly quantitative field.
- Experience level: 0 to 2 years. We welcome sharp, analytical graduates who can showcase a personal portfolio or GitHub repository of data-cleaning projects.
- FMCG/Retail exposure: Prior exposure to ERP systems (like SAP) or e-commerce data structures is an advantage, but strong technical execution is our priority.

Key Competencies

- The "Cleaning" Mindset: A true passion for turning chaotic, messy spreadsheets into flawless, structured data models.
- Logical Problem Solving: A methodical approach to debugging script errors and tracing missing data back to its source.
- Clear Documentation: Commitment to writing well-commented code and building clear "how-to" readmes for the business.

Pay: $62,838.15 – $92,828.02 per year

Benefits:

- Maternity leave
- Parental leave

Work Authorisation:

- Australia (Required)

Work Location: In person

📌 Junior Data Management Analyst (Drummoyne)
🏢 FMCG Analytics
📍 Drummoyne