Original listing text, shown exactly as published by the company.
The 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 & Strategy
- Define and own the multi-year technical vision and strategy for Analytics Engineering, ensuring alignment with Paddle's product and business objectives.
- Lead, coach, and develop a team of Senior Analytics Engineers, fostering a high-performing, inclusive, and growth-oriented culture.
- Act as a force multiplier across the group: identifying and removing structural blockers, elevating engineering standards, and scaling delivery capacity.
- Drive and champion best practices in analytics engineering, data modelling, data governance, and operational excellence across all teams.
Stakeholder Management
- Serve as the senior Engineering representative for Analytics Engineering with cross-functional leaders in Product, Finance, Sales, Marketing, and Data Platform.
- Build and maintain trusted, long-term relationships with stakeholders at Director and VP level, proactively aligning on goals, managing expectations, and communicating progress and risk with clarity and transparency.
- Navigate competing priorities across multiple senior stakeholders; 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.
We'd love to hear from you if you are
- 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.
Everyone is welcome at Paddle
At Paddle, we’re committed to removing invisible barriers, both for our customers and within our own teams. We recognise and celebrate that every Paddler is unique and we welcome every individual perspective. As an inclusive employer we don’t care if, or where, you studied, what you look like or where you’re from. We’re more interested in your craft, curiosity, passion for learning and what you’ll add to our culture. We encourage you to apply even if you don’t match every part of the job ad, especially if you’re part of an underrepresented group.
Please let us know if there’s anything we can do to better support you through the application process and in the workplace. We will do everything we can to support any accommodations needed. We’re committed to building a diverse team where everyone feels safe to be their authentic self. Let’s grow together.
