Analytics Engineer

About us

i6 provides the world’s most advanced end-to-end Aviation fuel management technology - enabling operational efficiency, transparency, and sustainability for airlines, fuel service providers, and suppliers. 

Our cloud-based solutions digitise the entire aviation fuel lifecycle through real-time fuel inventory and into-plane management platforms, patented electronic refuelling technology, and advanced fuel analytics and insights. 

With the adoption of our technology, our customers have been able to reduce thousands of tonnes of CO2 and benefited from millions in cost savings. 

Your new role

In your new role as an Analytics Engineer at i6 you will be responsible for designing, building, and maintaining complex data models using dbt (Jinja, Macros, Incremental strategies) and managing high-availability ingestion pipelines. You will focus on the "build" phase of the data lifecycle—implementing DataOps best practices, including CI/CD via GitHub Actions and automated testing frameworks. You will optimise warehouse performance (BigQuery/Snowflake) to support millions of rows of data and collaborate across teams to ensure data contracts are met and data quality is guaranteed before it reaches the end-user.

What you will do

Own the Transformation Layer: Design, build, and maintain complex dbt models to power internal BI and external customer analytics.

Pipeline Management: Manage and monitor data ingestion pipelines to ensure high availability and low latency.

Performance Tuning: Optimise cloud data warehouse costs and query performance (clustering, partitioning) for sub-second response times.

Data Quality & Testing: Build and maintain automated testing frameworks (dbt test, Great Expectations) to proactively catch data issues.

DataOps: Maintain CI/CD pipelines (GitHub Actions) for data deployment, applying software engineering principles to data workflows.

Collaboration: Partner with Data Analysts to provide clean models and work with the Data Engineering Lead on architectural infrastructure decisions.

Technical Documentation: Document data models, macros, and transformation logic clearly to ensure team scalability.

Who you are

3-4+ years of experience in Data or Analytics Engineering.

Expert SQL: Ability to write complex window functions, optimise joins, and debug query plans.

dbt Expertise: Deep hands-on experience in production (snapshots, incremental models, custom generic tests, Jinja/Macros).

Cloud Data Warehousing: Deep understanding of BigQuery or Snowflake, specifically clustering and partitioning strategies.

Version Control: Expert-level comfort with Git, branching strategies, and Pull Request workflows.

Orchestration: Experience with Airflow, Dagster, or similar tools.

NoSQL Knowledge: Understanding of NoSQL structures and how to transform them into relational models.

Data Contracts: Understanding of data contracts and their role in pipeline stability.

You will be a great fit for this role if in addition to the above you have the following:

Experience with Google Cloud Platform (GCP) and its data services (BigQuery).

Familiarity with Infrastructure-as-Code (Terraform).

Experience with Containerization (Docker).

Programming proficiency in Python for automation and data manipulation.

A bit more about us

We’ve recently raised our Series B funding.

We are a remote first company with offices in Farnborough and Manchester.  A number of our team are fully remote and some teams are primarily remote, typically meeting in the office once a month. 

We aim for all of the company to come together for a day once a quarter.

Our benefits include: 25 days annual leave + your birthday day off, private healthcare and 5% pension contribution.