Analytics Engineer

Company9fin

LocationLondon Area, United Kingdom

Posted At3/2/2026

UK Visa Sponsorship Analytics

Analytics are greyed out due to low classification confidence (41.0%).

Occupation Type

Telecoms and related network installers and repairers

Occupation Code Skill LevelMedium Skilled

Sponsorship Salary Threshold

£41,700 (£21.38 per hour)

Standard minimum applies

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Description

The Opportunity

The Analytics team is a critical engine driving 9fin's mission, and our data platform sits at its core. We are strong believers in building robust, reliable data foundations to empower data-driven decisions across the entire business. The Analytics Engineering team is responsible for designing, building, and maintaining our central data warehouse, creating the foundational data models that underpin analytics, reporting, and operational excellence across various departments, including Product, Finance, Operations, and more.

We are seeking a skilled Analytics Engineer to join us and elevate 9fin's data capabilities. In this role, you will focus on building and optimizing the core data warehouse, ensuring data quality, accessibility, and performance for a wide range of internal stakeholders. You will be instrumental in creating a trusted and well-governed data environment that enables informed decision-making at all levels of the organization. This is an opportunity to shape the very foundation of how 9fin leverages data for both strategic insights and operational efficiency.

What you'll work on

Every day is different but a key contributor to our data platform, you might:

Lead the Design and Evolution of dbt Models: Designing, building and refactoring robust staging, intermediate and mart layer models within BigQuery and dbt, ensuring they are scalable, well-tested and capable of supporting complex product, commercial and operational analytics use cases across the business.
Drive Metric Definition and Semantic Consistency: Owning the evolution of key business and product metrics, refining the semantic layer to ensure consistent KPI definitions across tools, and reducing ambiguity or duplication in how core metrics are calculated and consumed.
Strengthen End-to-End Data Flows: Partnering closely with Data Engineering to improve ingestion and transformation reliability, validating cross-system data movement, and proactively identifying and resolving structural weaknesses in ELT pipelines.
Integrate New Business Domains into the Warehouse: Scoping and implementing changes to data models as new systems, teams or initiatives are introduced, ensuring they are cleanly and sustainably integrated into our warehouse architecture.

Lead Complex Data Investigations: Tracing high-impact data issues from dashboards back to source systems, identifying root causes, and implementing durable, long-term fixes that improve overall platform reliability and trust.

Collaborate and Enable Decision-Making: Working directly with senior stakeholders across Product, Finance, Commercial and Operations to translate ambiguous business problems into well-structured, production-ready data models that enable confident, data-driven decisions.

Raise the Bar for Analytics Engineering: Contributing to modelling standards, testing frameworks and documentation practices, reviewing peer work, and mentoring mid-level engineers to continuously improve how we build and operate our data platform.

Contributing to the Roadmap -As a senior, proactively identifying valuable work to be done, owns architecture and quality of critical data domains. Helps to drive the definition and adoption of key company-wide metrics.

About You

This role will be a great fit if you:

Have strong data warehousing fundamentals:

You understand dimensional modelling and modern warehouse design principles, and you’ve applied them in production environments to build scalable, maintainable data models

Bring significant hands-on experience with dbt and cloud warehouses: You’ve designed and refactored complex staging, intermediate and mart-layer models in platforms such as BigQuery or Snowflake, and you optimise for clarity, performance and long-term maintainability.

Demonstrate deep SQL expertise: You are highly fluent in SQL, comfortable with complex transformations, window functions and performance optimisation, and able to reason clearly about query plans and trade-offs.

Have strong experience with semantic and BI layers: You have worked extensively with modern semantic or BI platforms (e.g. Omni, Cube or similar), understand how metric definitions propagate across tools, and have owned the design and maintenance of consistent, reliable KPI logic powering self-serve analytics.

Understand ELT and pipeline mechanics: You are comfortable working within modern ELT architectures, collaborating with Data Engineering on ingestion and orchestration, and troubleshooting issues that span from source systems to downstream models.

Have strong metric judgment: You’ve worked on defining and evolving core business or product metrics, understand the importance of semantic consistency, and know how to reduce ambiguity in KPI definitions across teams and tools.

Care deeply about data quality: You implement robust testing in dbt, proactively identify weak points in modelling logic, and prioritise clean, well-documented, production-ready data assets over quick fixes.

Are comfortable operating in ambiguity: You can take loosely defined business problems, ask the right questions, and translate them into structured, reusable data models without heavy direction.

Communicate clearly and influence effectively: You can articulate trade-offs, push back constructively when requirements are unclear or fragile, and build strong working relationships across Product, Finance, Commercial and Operations teams.

Operate with an AI-native mindset: You actively use AI tools to increase your leverage; accelerating SQL development, generating and refining dbt tests and documentation, automating repetitive workflows, and continuously experimenting with ways to improve productivity. You treat AI as a force multiplier in your daily work, not an afterthought.

Bonus points if you are:

Experienced with orchestration tools such as Dagster or Airflow

Proficient in Python for automation or tooling around the warehouse