Analytics Engineer (Fintech)

Location: London, hybrid

Company

hirify.global is an AI-first fintech and investment platform focused on exchange-traded funds (ETFs).

What you will do

• Design, build, and maintain dbt Core models that transform raw data into tested, documented, governed datasets.
• Own the semantic layer, including shared metric definitions, business logic, naming standards, and self-serve BI.
• Build ingestion and transformation pipelines using AWS, Redshift, Airflow, Python, and related data tooling.
• Develop the AI context layer so models, documentation, lineage, and metrics are machine-readable and produce consistent answers.
• Own warehouse performance, cost optimisation, data quality, freshness monitoring, CI, version control, and observability.
• Partner with Engineering and business stakeholders on source schemas, reporting, PII governance, access controls, and analytics requirements.

Requirements

• Hands-on production experience with dbt Core and strong SQL expertise.
• Solid Python experience for data engineering and automation, plus working knowledge of AWS data services.
• Experience with GitHub and GitHub Actions or equivalent CI/CD tooling for data code.
• Experience with a modern BI tool such as Lightdash, Omni, Looker, or Metabase, and interest in semantic layers and self-serve analytics.
• Comfort using AI tools in daily engineering work and communicating technical trade-offs clearly with non-technical stakeholders.
• Ability to operate with ambiguity and take ownership as the first dedicated analytics engineer.

Nice to have

• Airflow or similar orchestration tools and data ingestion tools such as dlt or Airbyte.
• Redshift warehouse cost and performance tuning experience.
• Fintech, investment management, or other regulated-industry experience.
• Experience handling PII through sanitised and restricted datasets, role-based access, or automated detection.
• Experience with Notion AI or Asana automations.

Culture & Benefits

• Small, fast-moving environment with significant ownership and visibility.
• Clear thinking, simple solutions, testing, documentation, and continuous improvement are emphasised.
• Direct collaboration with Product, Engineering, Risk, Finance, Investment, Marketing, and Operations.
• AI tools are used practically to accelerate engineering, documentation, quality assurance, and analytics.
• Data accessibility is balanced with appropriate privacy, governance, and least-privilege controls.