Job Purpose

• We are seeking a technically sophisticated and experienced Lead Data Analyst to drive data-driven decision-making within our Global Container Shipping division.
• In this pivotal role, she/he will act as the bridge between complex maritime operations and advanced data analytics. She/he will be responsible for architecting scalable data models in Google BigQuery, developing semantic layers in Looker, and leading a team of analysts to uncover insights related to fleet utilization, yield management, trade lane performance, and logistics optimization.
• This is a hands-on technical leadership role. She/He is expected to write complex production-level SQL, govern LookML codebases, and mentor junior analysts on best practices.

Primary Job Description (Describe the major duties of this position)

1. Technical Leadership & Data Strategy

• Lead, mentor, and review the work of a team of data analysts, ensuring high standards of code quality, query performance, and data accuracy.
• Establish best practices for SQL coding standards, version control (Git), and data documentation within the analytics team.
• Translate high-level business requirements from Commercial, Operations, and Logistics stakeholders into technical data specifications.

2. Advanced Analytics & Engineering (BigQuery)

• Design and execute complex, optimized SQL queries within Google BigQuery to handle huge scale shipping data (AIS data, bookings, bills of lading, equipment moves).
• Perform data modeling and transformation to prepare datasets for BI consumption, ensuring cost-efficiency and query performance in BigQuery.
• Investigate data discrepancies and work closely with Data Engineers to resolve pipeline issues.

3. Business Intelligence & Semantic Modeling (Looker)

• Serve as the primary owner of the Looker instance and LookML project.
• Architect and maintain efficient LookML models (views, explores, joins) to ensure a "single source of truth" for key shipping KPIs (e.g., Load Factor, Net Yield/TEU, Schedule Reliability, Detention & Demurrage).
• Design intuitive, high-performance dashboards that enable real-time monitoring of vessel performance and cargo flow.

4. Domain-Specific Analysis

• Conduct deep-dive analyses on profitability by Trade Lane, Service Loop, and Customer segments.
• Analyze container flow imbalances and empty repositioning costs to suggest optimization strategies.
• Monitor vessel schedule integrity and port congestion impacts using historical and real-time data.
  
  Position Qualifications and Experiences
  
  Experience
  
  • 5+ years of professional experience as a Data Analyst, Data Engineer, or Analytics Engineer.
  • Minimum 5 years of direct experience in the Maritime / Global Container Shipping industry. (Must understand concepts like Bills of Lading, TEU, Voyage/Leg, Port Pairs, Demurrage, Bunker consumption, etc.)
  
  Technical Skills (Must-Have)
  
  • Expert-level SQL proficiency: Ability to write window functions, complex joins, nested fields handling, and query optimization specifically for Google BigQuery.
  • Deep expertise in Looker: Proven experience in developing and managing LookML models and user permissions (not just building dashboards via UI).
  • Experience with version control systems (Git/GitHub/GitLab).
  
  Soft Skills & Leadership
  
  • Proven track record of leading projects or mentoring junior team members.
  • Ability to explain complex technical concepts to non-technical stakeholders (e.g., Line Managers, Trade Managers).
  • Strong problem-solving skills and attention to detail.
  
  Preferred Qualifications (Nice to have)
  
  • Experience with dbt (data build tool) for data transformation.
  • Proficiency in Python or R for advanced statistical analysis or forecasting.
  • Experience with Google Cloud Platform (GCP) ecosystem beyond BigQuery (e.g., Cloud Composer/Airflow, Cloud Functions).
  • Knowledge of supply chain logistics beyond port-to-port (e.g., intermodal/inland logistics).
  
  Tech Stack
  
  • Data Warehouse: Google BigQuery
  • BI & Visualization: Looker (LookML)
  • Version Control: Git
  • Transformation: SQL (Standard), dbt (preferred)