Analytics Engineer

• You’ll own and evolve the presentation layer of our data warehouse, optimising databases, schemas, views, and tables to power accurate, efficient reporting and analysis.
• You’ll design and maintain high-impact, self-serve data models that enable teams across the business to confidently make data-driven decisions.
• You’ll partner with BI and Analytics teams to deeply understand reporting needs and deliver models that fuel actionable insights and compelling narratives.
• You’ll continuously improve data model performance and warehouse design, making our data infrastructure faster, more scalable, and easier to use.
• You’ll set and uphold standards for data quality, governance, and documentation, ensuring consistency, clarity, and trust in our data assets.
• You’ll support strategic data initiatives, collaborating across teams to propose smart, scalable solutions and drive automation where it matters most.
• You’ll champion self-serve analytics, training and empowering colleagues to confidently explore and use data via our BI tools.
• You’ll mentor teammates, share knowledge, and contribute to a strong data culture through code reviews, planning, and open collaboration.

Senior Analytics Engineer

• Be the data analysis expert and developer for DOC Analytics. They will be used to using methodical investigation and design thinking to identify, shape and deliver value, working with the wider Product team on the most important challenges and opportunities.
• Be known for delivery. Own the end-to-end delivery of advanced analytics solutions within the DOC Analytics Product. Focus on the value and getting things done.
• Develop code that meets the needs of a highly-regulated organisation. The TDA will write code in line with current best practice, be comfortable taking challenge on their approach and designs, and deliver reliable solutions that our stakeholders trust.
• Define the problem, develop user stories (including the breakdown of work) and shape and constructively challenge the product owner and stakeholder requirements.
• Work in an agile manner as part of the team, using design thinking, breaking work into small increments, and building out patterns and processes that can be repeated.
• Present back to our stakeholders, demonstrating work-in-progress and articulating feedback and progress clearly.

• Set and enforce the data modelling standards that govern how the team designs facts, dimensions, aggregates, and semantic layers
• Review and exercise independent judgement on modelling approaches proposed by Lead Analytics Engineers, making formal recommendations where decisions carry material risk or long-term architectural consequence
• Own the technical integrity of the curated data layer – ensuring what is built is correct, performant, well-documented, and genuinely fit for business use
• Translate business requirements into data model designs that are both technically sound and analytically useful
• Act as the final technical quality checkpoint before work reaches production or is presented to business stakeholders
• Bring genuine analytical expertise: the ability to sit with data, identify what it is and is not telling you, and present findings in a way that is clear, honest, and actionable

Lead Analytics Engineer

• Ownership of data pipelines, data warehouse and BI tooling
• Sourcing, modelling and making available data required to underpin the company strategy and day to day activities
• Maintaining the accuracy and security of our data sources
• Managing stakeholder relationships and priorities in your area of focus
• Promoting pragmatic and effective use of data to drive decision-making
• Training stakeholders across the organisation to improve their use of data
• Supporting stakeholders by carrying out analysis requiring specialist knowledge
• Reducing ambiguity or reconciling disagreement between teams
• Encouraging a culture of experimentation and scientific rigour in the organisation
• Developing and mentoring other members of the team

Analytics Engineer, Mid Level

• Design and build scalable data solutions that connect systems
• Develop cloud-based data pipelines in Azure
• Deliver intuitive Power BI dashboards
• Support the development of an advanced analytics platform
• Lead the development of data models, reporting solutions and automated workflows
• Collaborate closely with stakeholders to translate business needs into effective data solutions
• Ensure reporting is insightful, reliable and future-ready
• Design and deploy Azure-based data solutions
• Develop and maintain Power BI dashboards
• Enhance reporting through automation using Microsoft Power Platform
• Take ownership of data quality, governance and performance

Data and Analytics Engineer

• Developing and enhancing Dynamics 365 using Power Apps, configuration and JavaScript
• Supporting opportunity, account, product and sales process functionality
• Improving user experience and ensuring CRM capabilities align to agreed sales processes
• Designing, building and optimising Power Automate workflows
• Reducing manual processes such as: Account and opportunity creation, Renewals and contract extensions, Data cleansing and approvals
• Supporting integrations across CRM, SharePoint and ETL tools
• Supporting acquisitions, data migrations and onboarding into core sales platforms
• Maintaining and improving product catalogue, pricing structures and sales templates
• Ensuring clean, auditable data structures that support downstream reporting and analytics
• Supporting SAP audit, commissions and financial reporting processes
• Ensuring CRM data feeds meet requirements for forecasting, planning and finance teams
• Providing second-line support for system issues and defects
• Proactively improving system stability, performance and resilience
• Supporting user adoption through fixes, enhancements and process improvements

Data & Analytics Developer – CRM, Power Platform

• - Help design, build and maintain the core data models that teams rely on day to day.
• - Improve self-serve analytics (You’ll help teams get faster, clearer answers from data without always needing bespoke analysis.)
• - Support customer and supplier journey insight (You’ll help us understand how customers and suppliers move through Togather’s marketplace, where journeys are working well, and where they can be improved.)
• - Build data products for people and AI tools (We’re increasingly using AI tools to help teams work faster and make better use of company knowledge. This role will help make sure those tools are built on reliable, governed and well-modelled data.)
• - Work closely with stakeholders (You’ll be a senior partner to teams across Togather, helping them understand what data can and can’t tell them.)

• Building and owning high quality dbt models, ensuring they are performant, well documented and thoroughly tested
• Rule Engineering: Partner with product engineering and business teams to translate business logic into programmatically enforced Data Quality Rules
• Collaborate with analysts and data scientists to understand their needs and build models that drive Data Quality and the visibility and measurability of Data accuracy.
• Champion best practices for Data Quality and data testing, across data engineering teams
• Review and work on Data Quality issues, both from a technical perspective and performing root cause analysis with stakeholders

Analytics Engineer, Data Quality Engineer

• Build and maintain well-structured, reusable data tables that serve as the source of truth for key business metrics, including ARR, product usage, and customer data.
• Translate commercial knowledge into correctly defined, consistent business logic that analysts and stakeholders can trust.
• Ensure interoperability across data sources by transforming and standardising data from across our product and tooling ecosystem.
• Manage and evolve our dbt codebase, maintaining high standards of code quality and documentation.
• Partner with the insights team on quarterly planning, prioritising data asset development to unlock analytical capacity.
• Proactively manage data changes — from product updates to source migrations — to maintain downstream consistency in Looker and beyond.

Data Analytics Engineer

• Define and execute the global vision and roadmap for analytics engineering, in particular evolving our data strategy to be AI first
• Lead a high-performing organisation, managing globally to foster a culture of technical excellence, delivery, and continuous growth
• Partner closely with senior stakeholders across Product, Engineering, and the Business to ensure analytics is embedded in every decision
• Drive 'Data as a Product', owning the evolution of our data models and semantic layer, ensuring teams can reliably self-serve from high-quality data products
• Champion governance and guardrails, establishing rigorous standards for modelling, semantics, testing, and auditability to ensure our data products are trusted and reliable.
• Maintain technical depth, staying hands-on enough to review SQL/data models, guide architectural decisions, and troubleshoot high-priority incidents when needed.

Analytics Engineering Director

• lead a dynamic team, working closely with various business units
• translate complex business requirements into actionable data solutions
• communicate analytics insights clearly for data-driven decision-making
• collaborate with Sales, Marketing, Finance, Product Managers, and Engineering teams
• define and own the multi-year technical vision and strategy for Analytics Engineering
• lead, coach, and develop a team of Senior Analytics Engineers
• advocate for investment in resources by constructing business cases for senior leadership
• ensure analytics data estate is treated as a product with clear ownership and quality standards
• oversee reliability, scalability, and accuracy of Paddle's analytics data estate
• maintain high standards of data governance, data quality, and documentation across the group
• create and maintain a culture of continuous learning

Head of Analytics Engineering

• Perform complex data manipulation and analysis to support senior business experts and stakeholders.
• Utilise Python and its data libraries to handle large datasets and automate analytical tasks.
• Design and execute robust data mapping and analysis projects.
• Work with our expert teams to understand client needs and translate them into effective data solutions.
• Working in a fast paced start-up agile and high pressure environments.

Mid Analytics Engineer, SQL, DBT

• Architecting High-Performance Models: You will design and deploy sophisticated data transformation pipelines using Dataform.
• Modernising Business Logic: You will lead the evolution of established business logic into modern, scalable SQL architectures.
• Strategic Technical Partnership: You will act as a consultative partner to BI, Data Engineering, and Data Science teams.
• Advancing Engineering Standards: You will champion and apply software engineering best practices to the analytics layer.
• End-to-End Delivery Leadership: You will own the full lifecycle of data product delivery.
• Championing Data Trust: You will be the standard-setter for data excellence.
• Enabling Data Lineage: You will build and maintain detailed documentation that illustrates data origin and transformation.

• Data Modelling & Transformation: Lead the development and maintenance of our dbt-enabled data models, transforming raw data into Silver and Gold layers within our Snowflake Medallion architecture.
• Product Delivery: Build and optimise the data structures that power our customer-facing BI products, ensuring high performance and accuracy for our external users.
• AI Integration: Architect and deliver high-quality data sets specifically designed to be consumed by both core applications, AI features and LLM-driven functionalities within the Arbor suite.
• AI-First Workflow: Embrace and pioneer the use of AI-assisted engineering tools to accelerate planning, architecting, and coding tasks, setting the standard for efficiency within the team.
• Collaboration: Work closely with Warehouse Data Engineers to ensure upstream data quality.
• Domain Exploration: Partner with Product Managers and domain experts to translate business requirements into technical schemas, bridging the product teams into the data function.
• Data Integrity: Support the wider data team in maintaining rigorous data quality and observability standards, utilising Datadog and dbt tests to ensure one version of the truth.
• Mentorship & Best Practice: Act as a Senior pillar for the analytics engineering community, performing code reviews and championing best practices.

• Own the transformation layer in dbt- design, build, and maintain modular, well-tested data models that define how data is structured and consumed across the company.
• Define and implement core business metrics (e.g. activation, engagement, retention) as reusable, versioned data assets- ensuring consistent definitions across analytics, product, and AI use cases.
• Model complex SaaS data by integrating product events, CRM (Salesforce), and support data into clean, well-defined fact and dimension models.
• Build and evolve our semantic layer- creating a reliable abstraction over our data that enables consistent KPI definitions and supports downstream consumers, including LLM-based analytics agents.
• Collaborate with Data Engineers on upstream data contracts and event schemas- ensuring raw data is structured in a way that supports scalable, reliable analytics.
• Establish and enforce best practices in testing, documentation, and data quality- making these part of the standard development lifecycle.
• Document models, metrics, and lineage clearly- enabling self-service and reducing ambiguity across teams.

• Collaboration
• - Collaborate with the Senior Data Engineer to design, develop, and maintain scalable data pipelines and ETL processes that support internal reporting and embedded analytics.
• - Partner with the data analysts to understand data needs, assisting in the creation of dashboards, reports, and KPIs.
• Data Solutions
• - Design and build data solutions to support reporting, analytics, and business insights for supply chain solutions.
• - Ensuring high data quality, integrity, and security across the different data solutions.
• - Participate in building dashboards and visualisations to communicate insights to stakeholders clearly and effectively.
• Solutions Lifecycle
• - Troubleshoot and resolve data-related issues, improving data flow efficiency and performance.
• - Evaluate and recommend improvements in our data tools, architecture, and practices, focusing on long-term scalability and adaptability.
• Data Pipelines
• - Support in designing, building, and maintaining advanced data models and data warehouses.
• - Writing, optimising, and reviewing SQL queries for data extraction and transformation.

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