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About the role
- Data Solutions Analyst ensuring accessible data for operational decision making in logistics sector. Collaborating with teams to design and maintain scalable data solutions.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- Health insurance
- Flexible work arrangements
- Professional development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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