Senior Analytics Engineer

The Role

As a Senior Analytics Engineer at Constantinople, you will design, build, and maintain the data models and transformation pipelines that power client reporting, regulatory reporting, and analytics across the business. You will work closely with cross-functional teams including Product, Risk, Finance, and Client Delivery to turn raw platform data into clean, well-modelled datasets the whole company can rely on, using dbt and Snowflake as the backbone of our analytics stack. We are a small team, so you will own meaningful pieces of work end to end and help set the standard for how we build.

What You'll Do

• Design, build, and maintain dbt models that transform raw data into clean, well-documented, analysis-ready datasets
• Write and optimise SQL in Snowflake, keeping models performant and cost-efficient as data volumes grow
• Own data quality within your models by implementing tests, documentation, and validation checks that catch issues before they reach downstream consumers
• Partner with Product, Risk, Finance, and Client Delivery to understand reporting needs and turn them into reusable data models rather than one-off queries
• Support and troubleshoot data issues affecting live customer reporting, including taking part in an on-call rotation
• Support the delivery of key metrics used in board, client, and regulatory reporting, where getting the numbers right matters
• Help maintain and evolve our modelling standards, naming conventions, and dbt project structure as the business grows
• Work with data and platform engineers on upstream pipelines, flagging data quality or structural issues at the source
• Take part in code reviews and help improve model quality, testing, and maintainability across the team

Who You Are

• You have hands-on experience building and maintaining data models in dbt, including tests and documentation
• You have strong SQL skills, with experience writing and optimising queries in Snowflake or a similar cloud data warehouse
• You have a solid understanding of data modelling concepts such as dimensional modelling and star schemas, and can apply them to messy real-world data
• You have worked with an orchestration tool such as Airflow to schedule and monitor pipelines
• You communicate well and are comfortable working directly with non-technical stakeholders to understand what they need
• You are detail-oriented and care about data quality, with a habit of catching issues before they become someone else's problem
• You are comfortable in a fast-paced start-up environment, pragmatic about scope and sequencing, and careful with data that people make decisions on