Analytics Engineer at Exinity

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

09 Apr, 26

Salary

0.0

Posted On

09 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Analytics Engineering, Python, SQL, Data Modelling, Machine Learning, ML Ops, Data Pipelines, BigQuery, Databricks, Workflow Orchestration, Data Integration, RETL, Data Quality, Problem Solving, Communication

Industry

Financial Services

Description

Company Description Exinity is a pioneering global FinTech company, deeply rooted in the financial services industry with a forward-looking approach to empowering a new generation of investors. Originating from FX brokerage, Exinity has expanded its horizons by developing innovative trading and investment products that cater to the unique needs of ambitious individuals globally. The company utilizes proprietary platforms and mobile technology to make financial independence accessible, embracing big data, blockchain innovations, and a multi-channel marketing approach with a significant advertising budget to drive its mission. Exinity's growth as a scale-up entity is propelled by its huge ambitions to redefine financial freedom for its global clientele. Job Description As an Analytics Engineer, you will play a central role in designing, building, and operating our Insight Environment. You will be responsible for developing reliable, scalable data pipelines, modelling data for analytical and machine-learning use cases, and ensuring high standards of data quality and observability across the platform. You will work across the analytics, data engineering, and ML lifecycle. Owning production-grade data transformations, orchestrating workflows, and supporting the deployment and monitoring of machine-learning models. While you will engage with event-level and marketing data where relevant, your primary impact will be in strengthening the engineering foundations that enable trusted analytics and ML at scale. Your work will directly support data-driven decision-making by ensuring our data and models are robust, performant, and production-ready. Key Responsibilities: Data Platform Ownership: Own and evolve core datasets and data domains within the Insight Environment, applying strong data governance, quality controls, and stewardship across the platform. Analytics Engineering & Data Modelling: Design and maintain production-grade data models and transformations using dbt and BigQuery, providing reliable, well-structured data for analytics, reporting, and downstream ML use cases. Machine Learning & ML Ops Enablement: Operationalise machine learning models and data science workflows in Databricks, supporting scalable deployment, monitoring, and lifecycle management of models in production. Workflow Orchestration & Reliability: Own the orchestration layer of the Insight Environment (Prefect), ensuring resilient, observable, and well-documented data workflows across ingestion, transformation, and activation. Data Integration & Activation: Build and manage data pipelines, including RETL and activation workflows (e.g. via RudderStack), to ensure timely and consistent data flow between analytical, operational, and ML systems. Qualifications You will have / be: Strong experience in data or analytics engineering roles, with advanced proficiency in Python and SQL for building and maintaining production-grade data pipelines and models. Solid working knowledge of PySpark or similar distributed computing frameworks in real-world data processing environments. A degree in computer science, data science, engineering, or a related field or equivalent professional experience demonstrating the same depth of technical capability. A practical understanding of how machine learning models are productionised, including deployment, monitoring, and lifecycle considerations. Proven experience in data preparation and modelling, with a strong focus on accuracy, reliability, and reusability across analytical and ML use cases. Experience designing and operating orchestrated data workflows, with an appreciation for reliability, observability, and maintainability. Familiarity with Reverse ETL concepts and data activation patterns, and the ability to apply them to real business problems. Strong problem-solving skills and the ability to communicate clearly and effectively with analytics, data science, and engineering stakeholders. Additional Information Why you'll love this role: In this role, you'll be at the forefront of data technology, working with an advanced modern data stack that includes industry-leading tools such as dbt, Databricks, BigQuery, and Prefect. You’ll not only apply these powerful tools to propel our data infrastructure forward but also continuously learn and master them. Our team thrives on innovation and efficiency, so you’ll have the chance to contribute to and shape our evolving data ecosystem. The role is designed to be a career-defining opportunity for a data enthusiast who is eager to explore the depths of analytics engineering and take ownership of projects that push the boundaries of what our data can achieve. Benefits 40 Days of Holiday, including Bank Holidays which you can take flexibly when you want. World class private health insurance with dental coverage. Significant “Flexible Benefits” budget to spend on the things that matter the most to you. Employee Assistance Program Life Insurance Critical Illness Insurance

Responsibilities

The Analytics Engineer will design, build, and operate the Insight Environment, focusing on developing data pipelines and ensuring data quality. They will also support machine learning model deployment and monitoring.