Analytics Engineer, Electra

• Solid SQL and dbt foundations, and the drive to build clean, maintainable transformation layers (we expect depth to scale with your experience)
• Solid data modelling fundamentals: you design clean, reusable schemas and know how to turn messy sources into trustworthy marts
• Comfortable on a modern cloud data warehouse (Snowflake or equivalent) and the surrounding ELT stack
• A genuine care for metric consistency and clear definitions (hands-on experience designing or governing a semantic and context layer is a plus)
• You care about reliability and quality: data testing, monitoring, anomaly detection, version control and CI/CD
• Trust is the product. You treat correctness and reliability as the deliverable - a metric people can rely on beats a clever model no one trusts
• Business-driven. You start from the decision the data needs to support, talk to stakeholders, and model for the consumer, not the pipeline
• Leverage over one-offs. You'd rather define a metric once and make it reusable than answer the same question ten times. You build foundations others build on
• Ownership beyond the model. You care what happens downstream: whether the dashboard is right, the alert fired, and the decision was made on good data
• Collaborative. You partner closely with other data team members, and business teams, and pick things up wherever they need to move
• Even if your profile doesn't match 100% of every criterion, we encourage you to apply. At Electra, we strive to create an environment where everyone feels comfortable, safe, and free to be themselves

Desirable

• Co-own the dbt transformation layer: design and maintain data models across business domains, with clear ownership, tests and documentation in our hub-and-spoke architecture
• Extend and reinforce our semantic and context layer: keep metric definitions governed and consistent, expand coverage to new domains, and enrich the business context so every metric is defined once and interpreted the same way - across our BI tools and by our analytics agent
• Enhance our analytics agent: shape the semantic and context layer so an LLM-powered agent can answer business questions accurately, with the guardrails and evaluation that make its answers trustworthy
• Model new domains as the business grows: onboard sources like SAP, electricity costs and more into well-structured, reusable data models
• Keep data trustworthy: harden our monitoring and alerting into robust metrics auditing and anomaly detection, so issues surface before stakeholders notice them
• Contribute to the data-platform foundations tied to analytics workloads: orchestration (Dagster) and ingestion (airbyte)