At a Glance
- Tasks: Lead analytics engineering initiatives and shape data solutions for impactful decision-making.
- Company: Join Moonpig Group, a leader in online gifting, spreading joy and connection.
- Benefits: Enjoy flexible working, generous holidays, private healthcare, and career growth opportunities.
- Other info: Collaborative culture with a commitment to diversity and inclusivity.
- Why this job: Make a meaningful impact on customer experiences through innovative data solutions.
- Qualifications: Extensive experience in analytics engineering and advanced SQL skills required.
The predicted salary is between 66150 - 80850 £ per year.
We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles. We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter – packed with personal touches and delivered with care. We’re not just about selling cards or gifts – we’re here to spread joy, spark smiles and make every celebration feel extra special.
At Moonpig Group, data powers how we create personalised experiences, make better decisions, and drive growth. As a Lead Analytics Engineer, you'll play a key role in shaping how data is modelled, governed, and consumed across the business. Operating at a business domain level, you'll combine deep technical expertise with commercial thinking to define, prioritise, and deliver analytics engineering solutions that enable scalable, trusted, and actionable data. You'll work closely with stakeholders across Product, Marketing, Finance, Engineering, Data Science, and the wider Data team, helping turn complex challenges into high-impact data products. As a technical leader within Analytics Engineering, you'll help set standards, mentor others, and influence the future direction of our data platform. This is an opportunity to make a meaningful impact on how Moonpig uses data to innovate, personalise customer experiences, and scale effectively.
Key Responsibilities:
- Lead the planning and delivery of analytics engineering initiatives across business domains, aligning work with strategic priorities.
- Own the delivery of scalable data models and datasets, coordinating contributions from other engineers where required.
- Partner with stakeholders across Product, Marketing, Finance, Data Science, and Engineering to define requirements and shape solutions.
- Challenge and influence stakeholders to drive scalable, sustainable, and high-impact data solutions.
- Act as a technical leader for analytics engineering, promoting best practices in data modelling, testing, documentation, and governance.
- Design and implement scalable, reusable data models using dbt and Snowflake.
- Lead architectural decisions, balancing performance, cost, scalability, and usability.
- Contribute to the evolution of analytics engineering standards, tooling, and data platform capabilities.
- Make and own technical decisions, balancing trade-offs between speed, scalability, and cost.
- Ensure high standards of data quality through testing, monitoring, and governance practices.
- Use metrics such as data quality, pipeline performance, and adoption to drive continuous improvement.
- Identify opportunities to optimise processes, tooling, and workflows.
- Translate complex business and data challenges into scalable data models and actionable delivery plans.
- Advocate for best practices in data modelling, governance, and data usage across the organisation.
- Represent Analytics Engineering in cross-functional discussions, helping teams navigate priorities and trade-offs.
- Mentor and support analytics engineers through code reviews, pairing, and knowledge sharing.
- Contribute to raising the overall quality, consistency, and maturity of the analytics engineering function.
- Support the optimisation and scalability of the Snowflake data platform, ensuring performance, security, and cost efficiency.
- Evaluate and introduce new tools and technologies that improve platform capability and engineering effectiveness.
- Drive adoption of standardised, well-documented data models across the business.
About You:
- Extensive experience in analytics engineering, data modelling, or related data disciplines, with a proven track record of operating across complex business domains.
- Advanced SQL expertise, including complex data modelling, transformation, and performance optimisation.
- Strong experience designing and maintaining scalable data models using dbt.
- Proven experience working with Snowflake and modern cloud-based data platforms.
- Deep understanding of analytics architecture, data governance, and scalable data design principles.
- Experience writing clean, efficient Python code for automation and data processing.
- Strong knowledge of Git and collaborative software development practices.
- Ability to influence stakeholders and build strong cross‑functional relationships.
- Experience operating with autonomy and making decisions in ambiguous environments.
- Passion for mentoring, coaching, and developing other engineers.
- Comfortable working within agile delivery environments.
- Curiosity for emerging technologies, tools, and best practices within the data ecosystem.
- Experience with DataDog, Dagster, Fivetran, or Tableau would be beneficial but is not essential.
Our Tech Environment:
- Infrastructure: AWS (SageMaker, EC2, Lambda, Glue, S3), Terraform, API Gateway.
- Analytics Tools: GA4, GTM, GCP BigQuery.
We don't expect you to have experience with every technology listed above. We're interested in people who are excited to learn, collaborate, and help us continue evolving our data capabilities.
What's in it for you?
- Wellbeing First: Private healthcare (UK) and mental health support.
- Flexible Working & Time Off: Generous holidays, hybrid working (1-3 days in office, depending on role/team) & up to 20 days of international working.
- Career Growth: Learning allowances, coaching & development programs.
Moonpig Group's Commitment to Equality, Diversity, and Inclusivity: At Moonpig Group, we’re all about creating a workplace where everyone feels they truly belong. We celebrate what makes each of us unique, whether that’s our background, how we work best, or what matters most to us. From working parents who need flexible hours to neurodiverse colleagues with specific working styles, we’re here to support our people in ways that work for them. Because when you feel valued and included, you can thrive, and so can we.
Analytics Engineering Lead employer: Women in Data®
Women in Data® is an exceptional employer that champions diversity and innovation within the tech industry. With a strong commitment to employee growth, you will benefit from generous annual leave, ongoing development opportunities, and a collaborative work culture that values mentorship and technical excellence. Join us in a role that not only enhances your career but also contributes to meaningful initiatives in data and machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineering Lead
✨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 Women in Data®!
✨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 Analytics Engineering Lead at Women in Data®.
✨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 Women in Data®.
✨Apply Directly through Our Website
When you find a suitable opening like Analytics Engineering Lead at Women in Data®, 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!
We think you need these skills to ace Analytics Engineering Lead
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 Women in Data®, 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 Women in Data®. 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 Women in Data®
✨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 Women in Data®!
✨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.
