At a Glance
- Tasks: Build and maintain scalable data pipelines for analytics applications.
- Company: Innovative tech company focused on data transformation and inclusivity.
- Benefits: Competitive salary, excellent benefits, and a commitment to diversity.
- Other info: Flexible work environment with opportunities for growth and collaboration.
- Why this job: Join a dynamic team and make an impact in data engineering.
- Qualifications: 3+ years in data engineering, proficiency in SQL, and experience with cloud platforms.
The predicted salary is between 50000 - 65000 £ per year.
The Data Engineering team has recently undergone a technology transformation, migrating from a legacy data warehouse to a brand‑new platform built on Snowflake, dbt, Argo Workflows and Kafka. We are looking for an Analytics Engineer to build and maintain scalable data pipelines that serve our analytics applications for Data Science, Machine Learning and Business Intelligence teams.
Key Responsibilities
- Design and implement robust data models (e.g., star schema, snowflake schema, data vault).
- Develop and maintain dimensional data models to support BI and reporting requirements.
- Develop and implement analytics solutions to track key performance metrics.
- Design and build data pipelines to collect, process, and store large volumes of structured and unstructured data from various sources.
- Develop and maintain data quality checks and validation processes.
- Automate reports, dashboards, and data visualisations to communicate insights and trends effectively to stakeholders.
- Build and maintain tooling and frameworks to automate data pipelines for experimentation and machine‑learning modelling.
- Develop and maintain a deep understanding of product domains to ensure relevant events are produced and new entities and processes are integrated downstream in the Snowflake data platform model.
- Monitor and troubleshoot data pipeline issues and provide timely resolution.
- Collaborate with product managers, data scientists, product analysts and software engineers to identify analytical requirements.
Requirements
- Bachelor's degree in computer science, engineering, mathematics or a related field.
- 3+ years experience in data/analytics engineering focused on building data pipelines.
- Proficiency in SQL and experience with Python or Java.
- Experience with modern cloud data warehouse platforms such as Snowflake, BigQuery, Redshift or similar.
- Experience with cloud‑based data platforms, particularly AWS or GCP.
- Experience with data warehousing, data modelling and ETL development.
- Strong analytical and communication skills and an understanding of how product performance drives commercial goals.
- Hands‑on experience with data visualisation tools such as Tableau, Looker, Streamlit or Power BI.
- Strong problem‑solving skills and attention to detail.
Preferred Skills
- Previous experience in similar analytics engineering roles focused on product analytics and data modelling.
- Experience working with distributed event stores and stream‑processing platforms such as Kafka or Kinesis.
- Experience with batch processing frameworks such as dbt, Argo Workflows or Apache Airflow.
- Familiarity with Docker, Kubernetes and Amazon EKS.
- Familiarity with continuous integration using GitHub Actions.
- Familiarity with test‑driven development and XP practices.
Benefits
- Competitive salary and excellent benefits.
Commitment to equity, diversity and inclusion. We are an equal‑opportunity employer and strive to create a fair and inclusive environment where every employee can thrive and bring their whole selves to work. We encourage diverse perspectives and are committed to supporting the growth and success of all team members.
Analytics Engineer in Uxbridge employer: 慨正橡扯
As an Analytics Engineer at our Uxbridge location, you will be part of a dynamic Data Engineering team that is at the forefront of technological transformation. We offer a competitive salary, excellent benefits, and a strong commitment to equity, diversity, and inclusion, fostering a work culture where every employee can thrive. With opportunities for professional growth and collaboration across various teams, you will play a crucial role in shaping our analytics capabilities while enjoying the flexibility to work from home.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer in Uxbridge
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works in analytics. Building relationships can open doors that job applications alone can't.
✨Show Off Your Skills
Create a portfolio showcasing your projects and skills. Whether it's a GitHub repo with your data pipelines or a blog post about your analytics solutions, let your work speak for itself. This is your chance to shine!
✨Ace the Interview
Prepare for interviews by practising common questions and scenarios related to analytics engineering. Be ready to discuss your experience with tools like Snowflake and dbt, and don't forget to highlight your problem-solving skills!
✨Apply Through Our Website
Don't forget to apply directly 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 genuinely interested in joining our team.
We think you need these skills to ace Analytics Engineer in Uxbridge
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Analytics Engineer role. Highlight your experience with data pipelines, SQL, and any relevant tools like Snowflake or dbt. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data engineering and how you can contribute to our team. Be sure to mention specific projects or experiences that relate to the job description.
Showcase Your Technical Skills:Don’t forget to showcase your technical skills in your application. Mention your proficiency in Python or Java, and any experience with cloud platforms like AWS or GCP. We love seeing hands-on experience with data visualisation tools too!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and we’ll be able to review your application quickly. Plus, it shows you’re serious about joining us at StudySmarter!
How to prepare for a job interview at 慨正橡扯
✨Know Your Tech Stack
Make sure you’re familiar with the technologies mentioned in the job description, like Snowflake, dbt, and Kafka. Brush up on your SQL skills and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled data pipeline issues or improved data quality in previous roles. Be specific about the challenges you faced and the solutions you implemented, as this will demonstrate your analytical abilities.
✨Understand the Business Impact
Be ready to discuss how your work as an Analytics Engineer can drive commercial goals. Think about how data models and analytics solutions can impact decision-making and performance metrics for the business.
✨Collaborate and Communicate
Since collaboration is key in this role, prepare to talk about your experience working with cross-functional teams. Highlight how you’ve effectively communicated insights to stakeholders and how you approach teamwork in data projects.
