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Gurugram, Haryana, India
Hybrid
Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape.
For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments.
Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected.
By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe.
This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead.
Wood Mackenzie
Intelligence Connected
Wood Mackenzie Values
Role Purpose:
The Senior Analytics Engineer plays a critical role in advancing the organisation’s analytics capability by designing, scaling, and governing high‑quality analytics-ready data assets on the Snowflake data platform using dbt (data build tool).
This role operates with a high degree of autonomy and is recognised as a technical specialist within analytics engineering, acting as a key bridge between data engineering, data analysis, and business stakeholders. In addition to hands-on delivery, the Senior Analytics Engineer provides technical leadership, shapes best practices, and enables others through mentoring, guidance, and standards.
The role has a strong focus on driving data quality, consistency, and scalability across domains, ensuring analytics outputs are trusted, performant, and aligned with business outcomes.
Key Responsibilities:
The successful candidate will be responsible for:
Advanced Data Modelling and Transformation
Designing, developing, and maintaining complex, scalable, and reusable data models and transformation layers in Snowflake using dbt, translating analytical and business requirements into robust multi-domain data structures. Applying Kimball dimensional modelling principles to create conformed dimensions, fact tables, and slowly changing dimensions for consistent, high performant analytics. Where appropriate, contributes to knowledge graph models that capture entity relationships, lineage, and semantic context to enrich analytical outputs and support discovery.
Platform & Workflow Leadership
Owning and optimising data transformation workflows to ensure analytics data is reliable, well-structured, and delivered to agreed quality and timeliness standards. Proactively identifies opportunities to improve performance, scalability, and maintainability of the analytics layer.
Technical Leadership & Enablement
Providing technical direction, coaching, and mentoring to Analytics Engineers and Data Analysts. Acts as an internal subject matter expert for dbt, analytics engineering patterns, and modelling best practices, enabling consistent adoption across teams.
Collaboration & Stakeholder Engagement
Working closely with data engineers, research, and business stakeholders to shape requirements, manage expectations, and deliver high‑impact analytics solutions. Engages confidently with senior stakeholders to influence priorities.
Analytics CI/CD & Development Practices
Owns or contributes to the dbt CI/CD pipeline, including environment strategy, deployment automation, slim CI, and state-based model selection. Ensures analytics code follows Git-based workflows with appropriate review, testing, and promotion practices.
Platform Cost & Performance Awareness
Designs models and transformation strategies that balance analytical richness with Snowflake compute efficiency. Applies incremental materialisation, clustering, and warehouse sizing considerations to manage cost and query performance at scale.
Data Quality, Governance & Standards
Defining and implementing data quality frameworks, testing strategies, and documentation standards across analytics datasets. Acts as a champion for data governance, challenging the status quo where appropriate and raising the maturity of data practices across teams.
Candidate Profile:
The ideal candidate combines deep technical expertise with strong communication and leadership skills, operating as a trusted advisor within the data organisation.
Essential Experience and Qualifications:
A bachelor’s degree in quantitative discipline such as Data Science, Computer Science, Engineering, or equivalent practical experience.
5+ years of hands-on experience in analytics engineering or a closely related data focused role, with proven experience in data modelling and transformation.
Expert-level SQL skills, with demonstrated experience designing and optimising complex analytical data models.
Proven experience working with Snowflake.
Strong, practical experience using dbt in a production analytics environment.
Strong understanding of Kimball dimensional modelling, including star schemas, conformed dimensions, and slowly changing dimension patterns.
Experience with Git-based workflows and CI/CD for analytics code (e.g., GitHub Actions, dbt Cloud CI, or equivalent).
Demonstrated ability to operate autonomously, owning complex analytics deliverables end-to-end.
Desirable Skills:
Proficiency in Python for data manipulation, automation, or testing.
Experience influencing or defining analytics engineering standards, patterns, or frameworks.
Strong understanding of data governance, data quality management, and analytics best practices.
Familiarity with BI and visualisation tools such as Power BI or Tableau, and how analytics models support effective reporting.
Experience mentoring or coaching other data professionals.
Ability to communicate complex technical concepts clearly to non-technical audiences.
A proactive mindset with a demonstrated ability to identify opportunities for improvement, automation, or innovation.
Experience working with knowledge graph models or semantic layers to represent entity relationships, business context, or data lineage.
Familiarity with Snowflake cost management, including warehouse configuration, query profiling, and incremental materialisation strategies.
Experience applying AI-assisted development tools (e.g., LLM-based code generation, automated documentation, or test generation) to analytics engineering workflows.
This role is suited to an experienced analytics engineering professional who is passionate about building trusted, scalable analytics foundations, influencing data practices at a broader organisational level, and enabling teams to extract maximum value from data.
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Equal Opportunities
We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race, colour, religion, age, sex, national origin, disability or protected veteran status. You can find out more about your rights under the law at www.eeoc.gov
If you are applying for a role and have a physical or mental disability, we will support you with your application or through the hiring process.