Website Wood Mackenzie

Location: Hybrid (Minimum 2 days per week on-site)
Industry: Renewables, Energy, and Natural Resources
Company Website: WoodMac.com

Wood Mackenzie is a global leader in data and analytics for the renewables, energy, and natural resources industries. Backed by 50+ years of expertise and enhanced by cutting-edge technology, Wood Mackenzie empowers companies and governments with insights that drive a sustainable and profitable future. With a team of over 2,400 professionals across 30 global locations, we deliver real-time analytics, consultancy, events, and thought leadership that help clients separate risk from opportunity.

We are looking for an experienced and highly skilled Senior Data Analyst to join our Metals & Mining Data team. If you are passionate about data, have strong analytical thinking, and enjoy solving complex challenges, this role is perfect for you.

🔎 About the Role

This role focuses on end-to-end BAU (Business-as-Usual) data operations, ensuring reliable delivery of critical data services while also contributing to data development and continuous improvement initiatives. You will be responsible for data analysis, automation, building pipelines, transforming datasets, and optimising daily data workflows.

You will serve as the subject matter expert for our data landscape—working with cross-functional teams, troubleshooting pipeline issues, enhancing data processes, and enabling stakeholders with accurate, timely insights.

🧠 Key Responsibilities

1. Data Development & Engineering

  • Design, enhance, and maintain automated data pipelines using SQL and Python.

  • Reverse-engineer existing scripts, optimise workflows, and rebuild scalable solutions.

  • Collaborate on designing data models, warehouses, and lakes aligned with product needs.

  • Integrate internal and external data sources via APIs, ETL systems, or web scraping.

  • Improve data frameworks by adopting modern cloud technologies like AWS and Snowflake.

  • Implement data validation, profiling, and quality checks to ensure reliability.

2. Data Analysis & Insights

  • Perform data discovery, cleansing, mapping, and transformation.

  • Provide deep-dive analysis to support research teams and product decisions.

  • Develop dashboards, KPIs, and reports to communicate insights.

  • Act as the primary expert for data-related queries across business units.

3. BAU Operations

  • Lead daily BAU processes ensuring consistent, accurate data delivery.

  • Create operational workflows, documentation, and monitoring systems.

  • Troubleshoot data pipeline failures, quality issues, and system errors.

  • Coordinate with engineering, research, and product teams to resolve critical incidents.

  • Drive automation to eliminate manual processes and improve efficiency.

4. Documentation

  • Maintain detailed documentation for pipelines, operations, and support processes.

  • Build and update a team-wide knowledge base for operational excellence.

5. AI Enablement & Automation

  • Explore AI/ML tools to enhance data extraction, quality checks, and analysis.

  • Support datasets preparation for model training and monitoring outputs.

  • Identify opportunities to automate recurring tasks and streamline processes.

6. Continuous Improvement

  • Analyse existing processes to identify bottlenecks and propose improvements.

  • Participate in retrospectives and contribute to building a culture of learning.

  • Implement solutions that enhance productivity and data reliability.

🎯 Personal Attributes

  • Strong analytical and problem-solving mindset.

  • Collaborative and comfortable engaging with both technical and non-technical teams.

  • Detail-oriented with a high level of accountability.

  • Proactive, adaptable, and capable of managing multiple priorities.

  • Clear communicator with excellent documentation skills.

🎓 Qualifications

  • Bachelor’s or Master’s degree in Computer Science, IT, Data Science, or a related field.

  • 8–10 years of experience in Data Analysis, Data Engineering, or BAU operations.

  • Experience in Metals & Mining or Energy sectors is a plus.

🌐 Additional Expectations

  • Hybrid working—2 days minimum onsite per week.

  • Flexible working hours to collaborate with global teams.

  • Not eligible for part-time or fully remote arrangements.

🤝 Equal Opportunity Employer

Wood Mackenzie is committed to diversity and equal opportunity in the workplace. We welcome applicants regardless of race, colour, religion, age, sex, nationality, disability, or veteran status.

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