Head of Analytics Engineering in Suffolk

Overview Paddle offers digital product companies a completely different approach to their payment infrastructure. Instead of assembling and maintaining a complex stack of payments-related apps and services, we’re a Merchant of Record for our customers. That means we take away 100% of the pain of payment fragmentation. It’s faster, safer, cheaper, and, above all, way better. We’re backed by investors including KKR, FTV Capital, Kindred, Notion, and 83North and serve over 5000 software sellers in 245 territories globally.

Role As an Analytics Engineering Manager, you'll lead a dynamic team, working closely with various business units to translate complex business requirements into actionable data solutions. You will play a critical role in communicating analytics insights clearly and with impact, enabling data-driven decision-making across the organization. Collaboration with Sales, Marketing, Finance, Product Managers, and Engineering teams will be essential to align analytics solutions seamlessly with business objectives and technical capabilities.

What you'll do

• Leadership make and communicate clear, well-reasoned trade‑off decisions that reflect both engineering and business considerations. Represent Analytics Engineering in Engineering leadership forums and contribute to shaping the broader engineering organisation. Advocate effectively for investment (headcount, tooling, infrastructure) by constructing well‑evidenced business cases and presenting them to senior leadership.
• Data as a Product Champion a product mindset for data across the organisation: ensuring Paddle's analytics data estate is treated as a first‑class product with clear ownership, defined consumers, and measurable quality standards. Lead the definition and governance of shared data definitions and business metrics, working cross‑functionally to drive alignment and eliminate ambiguity across teams. Ensure all data assets are well documented, well understood, and readily discoverable, including via MCP tooling, so that engineers, analysts, and business stakeholders can self‑serve with confidence. Define and enforce clear data ownership across the analytics estate, ensuring every dataset and model has an accountable team and a well‑understood purpose. Establish and continuously improve data quality standards: defining what "good" looks like, instrumenting quality checks throughout the pipeline, and ensuring issues are surfaced, triaged, and resolved systematically. Own the prioritisation of the Analytics Engineering roadmap, balancing strategic data product initiatives, platform reliability, data quality improvements, and stakeholder requests in a structured and transparent way. Build and maintain the processes by which new data needs are brought into engineering, evaluated, and sequenced, ensuring the group can absorb demand predictably and say no clearly when needed.
• Technical Oversight Oversee the reliability, scalability, and accuracy of Paddle's analytics data estate, including core data pipelines, data models, and transformation layers (Snowflake, DBT, Fivetran). Set the architectural direction for the group, guiding the most significant technical decisions and ensuring the analytics platform can scale with Paddle's growth. Maintain high standards of data governance, data quality, and documentation across the group.
• People Development Create and maintain a culture of continuous learning within the group, ensuring Senior Analytics Engineers are growing in both technical depth and professional skills. Provide regular, high‑quality feedback, set clear growth expectations, and identify high‑potential individuals early. Create and maintain training resources, onboarding pathways, and knowledge‑sharing rituals that systematically elevate the capability of the group. Actively identify skill gaps across the group and develop plans to address them through hiring, mentoring, or targeted development initiatives.

Requirements

• Have significant experience leading teams of senior engineers, with a track record of building and scaling high‑performing analytics engineering groups.
• Can demonstrate strategic ownership of a technical domain: setting direction, driving alignment, and delivering measurable outcomes at group or department level.
• Have a strong product mindset for data: you think about data assets in terms of consumers, quality, ownership, and discoverability, not just pipelines and models.
• Are an exceptional communicator with proven senior stakeholder management skills, including the ability to influence without authority, manage competing priorities transparently, and turn complex technical context into clear business language.
• Have deep expertise in modern analytics engineering (DBT, Snowflake, Fivetran or equivalent) and can provide strong technical oversight without being hands‑on day‑to‑day.
• Take a structured, principled approach to data governance: you have experience driving alignment on definitions, establishing ownership frameworks, and raising the bar on data quality at scale.
• Are familiar with AI coding assistants (such as GitHub Copilot, Cursor, or similar) and understand how they can meaningfully improve engineering productivity within an analytics engineering context.
• Have a working knowledge of semantic layers (such as dbt Semantic Layer, Cube, or LookML) and understand how they underpin reliable, consistent data access, including their role in enabling AI tooling and MCP‑based data discoverability.
• Are passionate about developing people and building systems, processes, and cultures that enable others to do their best work.
• Are proactive, self‑aware, and continuously looking for ways to improve both yourself and the organisation around you.