Hybrid Data Solution Analyst

at APG eCommerce Solutions

• Job title: Data Solution Analyst
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, BigQuery, Cloud, ETL, Google Cloud Platform, Python, SQL, Tableau
• Location requirements: Hybrid, Amman, Jordan

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