At a Glance
- Tasks: Design and maintain data pipelines, ensuring accurate analytics for decision-making.
- Company: Join Beauty Pie, a dynamic e-commerce retailer embracing AI and innovation.
- Benefits: Enjoy hybrid working, generous holiday, health support, and discounts on products.
- Other info: Collaborative environment with opportunities for mentorship and career growth.
- Why this job: Be part of an exciting data journey within the Shopify ecosystem and make a real impact.
- Qualifications: Experience in data engineering, strong SQL, Python skills, and a passion for problem-solving.
As a Senior Data Analytics Engineer at Beauty Pie, you will be responsible for designing, developing, and maintaining robust data pipelines and analytics solutions that empower stakeholders to self-serve where possible. You will collaborate closely with data analysts and business stakeholders to ensure the availability and accuracy of data needed for decision-making. Your expertise in data engineering, analytics, and software development will be crucial in driving our data strategy forward.
Beauty Pie is a subscription-based e-commerce retailer, and we have recently migrated our storefront to Shopify. This is an exciting time to join! You will play a key role in building out and maturing our data platform within the Shopify ecosystem, integrating data from Shopify and its surrounding ecosystem of tools and platforms. We move fast, but deliberately. We'd rather pilot something quickly and learn from it than spend weeks perfecting a plan. If you thrive in an environment where priorities shift, new ideas are tested rapidly, and you're trusted to use your judgement, you'll fit right in. We are an AI-first team. We actively use AI tools such as Claude Code to accelerate our development workflow, and we expect you to embrace AI-assisted development as a core part of how you work. Equally important is your ability to be the human in the loop by critically reviewing AI-generated output, applying sound engineering judgement, and knowing when to trust vs challenge.
Job Requirements
- Significant experience in data engineering, analytics engineering, or a related role.
- Recognised subject matter expertise in at least one area of the data stack, with a strong working knowledge across the rest.
- Passionate about helping stakeholders to solve business problems.
- Excellent SQL and data transformation knowledge.
- Experience with dbt or similar data modelling frameworks.
- Strong Python skills.
- Experience with Snowflake or similar cloud data warehouses (Databricks, BigQuery).
- Knowledge of data warehousing concepts, Kimball, Inmon & Data Vault.
- Experience with data visualisation tools e.g. Looker, Lightdash or Tableau.
- Proven experience in data ops (CI/CD, testing, orchestration, observability).
- Ability to lead cross-functional technical initiatives and influence without authority.
- Experience with infrastructure as code (Terraform) is a plus.
- Experience with workflow orchestration tools such as Airflow is a plus.
- Experience with event-driven data architectures and real-time analytics is a plus.
- Experience working with Shopify or e-commerce data is a plus.
- Strong communication and collaboration skills, with the ability to adapt your style for different audiences including senior stakeholders.
Our Tech Stack
- Cloud: AWS
- Infrastructure as Code: Terraform
- Orchestration: Airflow (MWAA)
- Data Warehouse: Snowflake
- Data Modelling: DBT
- Data Ingestion: DLT (Data Load Tool)
- Language: Python
Job Responsibilities
- Design, build, and maintain scalable data pipelines to support various data analytics and self-serve needs.
- Develop and implement ELT (Extract, Load, Transform) processes to integrate data from multiple sources into our data warehouse.
- Ensure data quality, consistency, and reliability through rigorous testing and validation procedures.
- Collaborate with analysts to understand data requirements and deliver actionable insights.
- Optimise and tune SQL queries and database performance to handle large volumes of data efficiently.
- Create and maintain documentation related to data architecture, processes, and workflows.
- Build observability into data pipelines and models from the outset including monitoring, alerting, logging, and data quality checks so issues are detected early rather than reported by stakeholders.
- Champion continuous improvement by proactively identifying bottlenecks, introducing process changes, and automating repetitive tasks to help the team move faster and more efficiently.
- Stay up-to-date with emerging technologies and best practices in data engineering and analytics.
- Work closely with cross-functional teams to align data initiatives with business goals and objectives.
- Facilitate complex technical discussions and decisions that impact multiple teams, bringing best practice and getting buy-in by demonstrating the value of change.
- Mentor and support junior team members, sharing expertise and helping to raise the technical bar across the team.
Job Benefits
- Hybrid working with 3 days in the office in central London
- Free membership to Beauty Pie+ and additional percentage off our products
- 25 days holiday & your birthday off
- Flexible bank holidays
- Equal leave for all new parents regardless of gender or personal circumstances
- Health & Wellbeing
- Private Medical Insurance
- Menopause support
- £2,500 / $2,500 to spend on your fertility journey after 2 years of service
- 10 therapy sessions through AXA PPP
- Access to mental health support through Spill
Apply now for a chance to be part of an inspirational, international and talented team. Beauty Pie is an equal opportunity employer. The company will not unlawfully discriminate on grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, religion or belief, colour, nationality, ethnic or national origin, disability or age, pregnancy or trade union membership.
Senior Data Analytics Engineer employer: Beauty Pie
At Beauty Pie, we pride ourselves on being an innovative and dynamic employer that fosters a collaborative work culture where creativity and agility thrive. As a Senior Data Analytics Engineer, you'll enjoy the benefits of hybrid working in the heart of London, alongside generous perks such as private medical insurance, flexible leave policies, and a commitment to employee wellbeing. Join us in shaping the future of e-commerce while advancing your career in a supportive environment that values continuous learning and personal growth.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Analytics Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Beauty Pie!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Analytics Engineer at Beauty Pie.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Beauty Pie.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Analytics Engineer at Beauty Pie, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Beauty Pie, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Beauty Pie. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Beauty Pie
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Beauty Pie!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
