Analytics Engineering Lead (dbt)

Location: London, hybrid with office attendance 1–2 days per week

Company

hirify.global Group uses data to create personalised experiences, support business decisions, and drive growth.

What you will do

• Lead analytics engineering initiatives across business domains and align delivery with strategic priorities.
• Design, implement, and maintain scalable, reusable data models and datasets with dbt and Snowflake.
• Partner with Product, Marketing, Finance, Data Science, Engineering, and Data stakeholders to define requirements and shape solutions.
• Set standards for data modelling, testing, documentation, governance, and platform usage.
• Lead architectural decisions while balancing performance, scalability, usability, speed, and cost.
• Mentor analytics engineers through code reviews, pairing, coaching, and knowledge sharing.

Requirements

• Extensive experience in analytics engineering, data modelling, or related data disciplines across complex business domains.
• Advanced SQL expertise, including complex transformations, data modelling, and performance optimisation.
• Strong experience designing and maintaining scalable data models with dbt and Snowflake or modern cloud data platforms.
• Understanding of analytics architecture, data governance, scalable data design, testing, monitoring, and data quality.
• Experience writing clean Python code for automation and data processing, plus strong knowledge of Git and collaborative software development.
• Ability to influence stakeholders, work autonomously in ambiguous environments, mentor engineers, and operate in agile delivery settings.

Nice to have

• Experience with DataDog, Dagster, Fivetran, or Tableau.
• Familiarity with AWS services, Terraform, API Gateway, GA4, GTM, or GCP BigQuery.

Culture & Benefits

• Permanent employment with a hybrid working model.
• Opportunity to influence analytics engineering standards, tooling, and data platform capabilities.
• Cross-functional collaboration across Product, Marketing, Finance, Engineering, Data Science, and Data teams.
• Focus on learning, collaboration, emerging technologies, and continuous improvement.

Hiring process

• Recruiter screening call.
• Hiring manager interview and technical test.
• Final interview with wider team members.