Staff Analytics Engineer

About the role

TEM is building AI native infrastructure to modernize electricity transactions and improve market transparency. As a Staff Analytics Engineer, you will design the domain models and semantic layers that power commercial, financial, and operational decisions across the company. You will work as an individual contributor to define technical standards and ensure our data infrastructure scales alongside our international expansion. In this capacity, you will own the full lifecycle of critical data products from conception through productionization. You will be responsible for establishing guardrails that ensure data integrity and consistency across all business units. You will partner closely with leadership to align analytical frameworks with strategic objectives and market realities. Your work will directly influence how stakeholders interpret performance, risk, and opportunity in the energy marketplace.

Key facts

What you'll do

• Establish and enforce high standards for testing, documentation, and modeling patterns across the organization, ensuring that best practices are followed consistently.
• Develop a trusted domain layer that provides consistent context for both human users and AI agents, enabling reliable interpretation of data.
• Collaborate with product managers, engineers, and sales teams to translate complex business requirements into reliable data models that accurately reflect operational reality.
• Integrate new data sources and platform events to expand the reach of the analytics engineering function, improving coverage and insight depth.
• Architect data systems capable of supporting global growth and new product offerings, ensuring scalability and performance under increased load.
• Maintain the semantic layer to ensure metrics are defined accurately and used confidently across the business, preventing misinterpretation and inconsistency.
• Implement robust data pipelines that handle the complexities of electricity market data, including temporal nuances and regional variations.
• Optimize query performance and data processing workflows to reduce latency and improve user experience across analytical tools.
• Define and manage conformance rules that ensure data quality remains high as systems evolve and new features are introduced.
• Mentor junior analysts and engineers on effective modeling techniques and data stewardship principles to elevate the entire team.
• Partner with data science teams to ensure analytical foundations support advanced modeling and machine learning initiatives.
• Evaluate emerging tools and technologies to identify opportunities for improving the analytics stack and infrastructure.
• Document data architectures and decision rationales to enable continuity and knowledge transfer across the organization.
• Act as a technical authority on data matters, providing guidance and recommendations to cross-functional stakeholders.

Requirements

• Proven experience building and refining domain layers in a production environment, with a track record of successful deployments.
• Advanced proficiency in dbt, including custom macros and optimization of complex models that handle large volumes of data.
• Strong SQL skills with experience managing data at scale in a modern warehouse, demonstrating ability to write efficient and maintainable queries.
• Hands-on experience with semantic layers or BI modeling tools such as Omni or Looker, showing ability to bridge business and technical needs.
• High level of QA discipline and attention to detail when defining metrics and data structures, ensuring accuracy and reliability.
• Demonstrated ability to work independently while maintaining alignment with team standards and organizational goals.
• Experience navigating complex business environments where data requirements evolve quickly and ambiguity is common.
• Comfort with iterative development processes and the ability to deliver value incrementally while improving underlying systems.

Nice to have

• Background in energy markets or industries with significant physical or financial complexity, providing context for domain-specific challenges.
• Experience managing commercial data, such as CRM pipelines, financial trading, risk, or forecasting, which mirrors the complexity of energy transactions.
• History of implementing quality standards or tooling that improved team-wide output, showing ability to drive process improvements.
• Ability to manage stakeholders by clarifying project scope and requirements early in the process, reducing rework and misalignment.

Practical notes

The interview process typically spans 2 to 3 weeks and includes a talent screening, a behavioral interview with the Analytics Engineering Manager, a technical team exercise, and a final stakeholder discussion. We encourage applications from all backgrounds and welcome candidates who may not meet every listed requirement. The role is fully remote within Europe, offering flexibility in work location while requiring reliable connectivity and self directed work habits. No business travel is required for this position, and candidates should be prepared to manage their own schedule in alignment with team expectations. The recruitment timeline is designed to be thorough yet efficient, respecting the time of all participants involved.