Senior Analytics Engineer - Product & Marketing >

Location

United Kingdom - London

Company

Redpin

About The Company

At Redpin, our mission is to bring global citizens and businesses together by simplifying life’s most important payments. Our world-class payments network and human-centric customer service touches a vast global community across 15,000+ partners, 235 territories and two flagship brands, Currencies Direct and TorFX. We are reshaping the age-old property market by connecting the dots across the entire ecosystem, from buyers and sellers to legal service firms and title companies to property management, banks, and real estate agents.

Our goal is to become the leader in payments and embedded software for international property. We are a diverse team of over 800 employees across 10 offices worldwide, working together to connect the dots for hundreds of thousands of customers around the world. Our B2B and Real Estate solutions are designed to streamline transactions and enhance user experience across the globe.

About the Role

Are you passionate about turning raw data into actionable insights and enabling data-driven decision-making? As an Analytics Engineer focusing on Product & Marketing, you will play a critical role in shaping our data ecosystem and ensuring that our teams have access to accurate, well-structured, and timely insights. You’ll collaborate closely with Product, Marketing, and Engineering teams to design and implement scalable data models, track key performance metrics, and optimize data workflows. Your work will directly impact how we measure product success, marketing effectiveness, and customer engagement.

What You'll Do

Data Foundations & Tracking

• Work with Product Managers to review Product Requirement Documents (PRDs), ensuring that success metrics and data requirements are clearly defined for new feature releases.
• Partner with Product Engineering to ensure all necessary event tracking and instrumentation is implemented correctly across platforms.
• Implement and maintain Google Tag Manager (GTM) configurations for product and marketing tracking.
• Manage and enhance our Google Analytics setup, ensuring accurate data collection and reporting.
• Develop and maintain marketing data models, integrating data from multiple sources to track campaign performance across channels. This includes data from GA (web analytics) and Google AdWords (Campaign and Ad Spend).
• Implement and refine multi-touch attribution models to measure the effectiveness of various marketing efforts.
• Support performance marketing teams with campaign analytics, enabling precise measurement of ROI and customer acquisition costs.
• Use dbt (Data Build Tool) to build, test, and optimize scalable data models for both product and marketing analytics.
• Work with cloud data warehouses (BigQuery, Snowflake, or Redshift) to ensure efficient data transformation and storage.
• Implement automated data quality checks to maintain integrity and reliability across datasets.
• Partner with cross-functional teams to democratize data access and improve self-serve analytics capabilities.
• Support A/B testing and experimentation frameworks, helping teams interpret results accurately.
• Create and maintain clear documentation for data models, metrics, and best practices.

What You’ll Need

• 3+ years of experience in analytics engineering, data engineering, or a similar role.
• Strong SQL skills and experience working with data warehouses (BigQuery, Snowflake, or Redshift).
• Hands-on experience with dbt for data modeling and transformation.
• Familiarity with Google Analytics, Google Tag Manager (GTM), marketing analytics tools, and any other Web Tracking platforms (Heap, Segment, etc.).
• Experience with marketing attribution models and tracking campaign performance across different channels.
• Strong understanding ofpredefined and custom eventtracking best practices, including implementation in collaboration with engineering teams.
• Ability to work cross-functionally with Product, Marketing, and Engineering teams.
• Excellent problem-solving skills and a structured approach to data challenges.
• Strong written and verbal communication skills to translate complex data concepts for non-technical stakeholders.
• Self-motivated and eager to optimize and automate data processes wherever possible.

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