About The Role

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The Data Solutions Analyst plays a pivotal role in ensuring that the right data is available, accessible, and
actionable to support strategic and operational decision making across network operations. This role is
responsible for maintaining and optimizing the data tools, systems, and solutions that enable reporting,
analysis, planning, and operational insight driving efficiency, data quality, and business performance.
The Data Solutions Analyst is tasked with designing, developing, and maintaining analytics ready datasets
and scalable data solutions that bridge the gap between raw data and usable insights. They collaborate
closely with data engineers, analysts, and business stakeholders to transform complex data into clean, well
documented models that support dashboards, advanced analytics, and planning tools. A strong focus on data
quality, governance, and scalability is essential to ensure that solutions remain robust and adaptable to
evolving business needs.

Contribute analytical insights that support commercial decision‑making and strategic business priorities.
Align data solutions, models, and pipelines with key operational and strategic objectives.

Design and implement well‑structured, analytics‑ready data models to support reporting, analysis, and business decision‑making.
Build and maintain robust transformation logic—primarily using SQL (BigQuery) and Confluence—to ensure clean, consistent, and trusted data outputs.
Ensure data models follow best practices for scalability, clarity, and maintainability.

Develop, maintain, and optimise scalable data pipelines using tools such as Airflow/Composer and BigQuery.
Implement CI/CD best practices for analytics workflows, including automated testing, validation, and version control (e.g., Git).
Manage ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes for efficient and reliable data flow from source to target systems.

Partner closely with data analysts to understand business needs and ensure data assets support analytical use cases.
Respond to stakeholder requirements by updating, extending, or enhancing datasets as business needs evolve.
Work cross-functionally to ensure alignment on data definitions, metrics, and analytical frameworks.

Implement data testing frameworks and validation processes to ensure accuracy, consistency, and integrity across datasets.
Maintain clear documentation for data models, transformation logic, and pipeline changes.
Support adherence to data governance standards and contribute to continuous improvement in data quality.

Monitor the performance of data models, queries, and pipelines to reduce latency and operational cost.
Optimise system performance by applying cloud analytics best practices, particularly within GCP environments.
Identify performance bottlenecks and recommend enhancements.

Perform advanced analytics and statistical modelling to uncover trends, correlations, and operational patterns in real‑time and historical data.
Conduct Root Cause Analysis (RCA) to identify and resolve data anomalies impacting operational performance.
Apply DMAIC (Define, Measure, Analyse, Improve, Control) principles to lead structured problem‑solving and continuous improvement initiatives.
Use insights to prevent issue recurrence and enhance data integrity and operational efficiency.

Document transformation logic, lineage, assumptions, field definitions, and changes clearly to support team transparency and reproducibility.
Contribute to shared Data Solution Analytical standards and data governance frameworks.
Ensure documentation supports long‑term maintainability and cross‑team collaboration.

Translate complex data outputs into clear, actionable insights that inform commercial and strategic decisions.
Partner with stakeholders to define KPIs and build analytical frameworks supporting business performance measurement.
Communicate emerging trends, patterns, and potential risks clearly to leadership and operational teams.
Design and maintain analytical tools and solutions that highlight opportunities, anomalies, and performance trends across network operations.
Collaborate with reporting and dashboard teams to deliver insights that drive lasting strategic change and business value.

2+ years of experience in data analysis, preferably in the logistics or supply chain industry.
Strong ability to interpret and manipulate large and complex data sets.
Proficiency with data tools and technologies, including:
GCP (BigQuery, Cloud Run Functions), Composer/Airflow, Python (and R), SQL, Excel Git / GitLab Runner, Confluence, Jira, Tableau
Understanding of logistics operations, including transportation, inventory, and distribution.
Excellent communication skills, capable of conveying complex analysis clearly to stakeholders.
Strong attention to detail with a commitment to accuracy and high-quality outputs.
Ability to work independently and collaboratively in a fast‑paced environment.
Experience working within Lean, Agile, or traditional project delivery methodologies.
Strong technical documentation skills, including the ability to create and maintain clear and structured analytics documentation.
A proactive, problem-solving mindset with a passion for data-driven decision making.
Ability to manage multiple priorities and tight deadlines.
Experience in continuous improvement or process optimisation.

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Data Solution Analyst P&C