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For Companies At a Glance
- Tasks: Design and build scalable data pipelines for analytics and machine learning.
- Company: Join a cutting-edge tech company focused on data innovation.
- Benefits: Enjoy 40 days of holiday, private health insurance, and flexible benefits.
- Why this job: Be at the forefront of data technology and shape our evolving data ecosystem.
- Qualifications: Experience in data engineering with strong Python and SQL skills required.
- Other info: Career-defining opportunity for data enthusiasts eager to innovate.
The predicted salary is between 36000 - 60000 ÂŁ per year.
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. 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
In this role, you will 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.
Analytics Engineer employer: Exinity
Contact Detail:
Exinity Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and models. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with Python, SQL, and any tools like dbt or Databricks. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are keen to join us directly.
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python, SQL, and any data engineering tools you've used. We want to see how you can bring your technical skills to the table, so don’t hold back!
Tailor Your Application: Take a moment to customise your application for the Analytics Engineer role. Mention specific projects or experiences that relate to data pipelines, machine learning, or data modelling. This shows us you’re genuinely interested in the position.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it’s relevant. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Exinity
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like dbt, Databricks, and BigQuery. Brush up on your Python and SQL skills, as you'll likely be asked to demonstrate your proficiency in building data pipelines during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you tackled them. Use examples that highlight your experience with data modelling, machine learning, and workflow orchestration to show you can think critically and solve real business problems.
✨Understand Data Governance
Familiarise yourself with data governance principles and quality controls. Be ready to explain how you’ve applied these in past projects, as this role emphasises the importance of maintaining high standards across the data platform.
✨Communicate Clearly
Practice articulating your thoughts clearly and effectively, especially when discussing technical concepts. You’ll need to engage with various stakeholders, so being able to convey complex ideas simply will set you apart from other candidates.
