About This Role
This foundational role is for the first dedicated Analytics Engineer at InvestEngine, sitting at the intersection of data engineering, analytics engineering, and business partnership. You will transform raw, disparate data into governed, trustworthy models that the rest of the company uses for decision-making. The role suits a technically strong individual with a strong appetite to shape the data space and make a real impact.
Responsibilities
- Design, build, and maintain dbt Core models that transform raw data into clean, well-tested, well-documented datasets
- Own and evolve the semantic layer, including consistent metric definitions, business logic, and naming that the whole company can trust and query against
- Establish and enforce data modelling standards, testing practices, and documentation as the function scales
- Evaluate and help roll out modern BI tooling to make metrics genuinely self-serve for non-technical stakeholders
- Structure models, metric definitions, documentation, and lineage to be machine-readable for AI tooling
- Make AI-driven analytics reliable by ensuring consistent answers to business questions
- Treat naming, documentation, and metric definitions as first-class engineering deliverables
- Use Claude to accelerate development, including model scaffolding, code review, documentation, QA, and pipeline debugging
- Use Notion AI to keep documentation and runbooks current and useful
- Bring a point of view on where AI can responsibly speed up analytics engineering and help the wider team adopt it well
- Build and maintain ingestion and transformation pipelines across the stack, including AWS, Redshift, and Airflow
- Write production-quality Python for extraction, transformation, and automation tasks outside dbt's remit
- Challenge and improve the existing data stack
- Own the performance and cost profile of the Redshift warehouse
- Spot and act on opportunities to reduce complexity and cost across models and pipelines
- Make deliberate trade-offs between freshness, cost, and complexity, and explain them to the business
- Champion analytics engineering best practice across the company, including testing, version control, code review, CI, and documentation
- Build and own data quality, freshness, and pipeline monitoring
- Set the standard for trustworthy data and maintain it as the volume of models and requests grows
- Work with engineering teams to ensure application and event design accommodates reporting and analytics requirements
- Influence payload and schema design for new services and product features
- Support analysts and business users with automation, tooling, and data engineering expertise, and provide training and guidance
