Data Analytics Engineer

Job Description

As 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

• Solid experience in data engineering, analytics engineering, or a related role.
• Depth of subject matter expertise in at least one area of the data stack, with a good working knowledge across the rest.
• Passionate about helping stakeholders to solve business problems
• Strong SQL and data transformation knowledge
• Experience with dbt or similar data modelling frameworks
• Solid Python skills
• Experience with Snowflake or similar cloud data warehouses (Databricks, BigQuery)
• Good understanding of data warehousing concepts such as Kimball, Inmon or Data Vault
• Experience with data visualisation tools e.g. Looker, Lightdash or Tableau
• Practical experience with data ops practices (CI/CD, testing, orchestration, observability)
• Ability to communicate concepts clearly to both technical and non-technical audiences
• 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 influence others and work effectively across teams.

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.
• Drive continuous improvement by proactively identifying bottlenecks, suggesting and implementing 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.
• Contribute to technical discussions and decisions, analysing challenging problems and proposing well-reasoned solutions.
• Support and guide less experienced team members, sharing knowledge and helping to raise standards 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

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