Analytics Engineer

• dbt Development: Scale our dbt project, shaping its architecture and CI/CD processes to support company-wide initiatives.
• Data Modelling & Architecture: Contribute to our data modelling strategy, helping to build and maintain the core set of performant, scalable, and trusted data models that power all downstream consumers—from our internal BI tool to our customer-facing agentic systems and analytics APIs.
• Data Quality & Governance: Champion and implement robust data quality standards, PII governance, testing, and documentation to ensure our data is trustworthy and reliable as we scale.
• Stakeholder Partnership: Partner with commercial (Sales, Finance) and product teams on data needs, assisting in translating business questions into data requirements and helping to manage delivery expectations.
• BI & Self-Service Enablement: Contribute to the development and refinement of foundational data assets in our BI tool (Lightdash), helping stakeholders and analysts access insights effectively.

• Design and develop analytics-ready data models and semantic layers in Snowflake
• Build scalable data transformations and automated data quality processes
• Create trusted datasets and standardised KPIs that support consistent reporting
• Partner with Data Engineers, BI Developers and business stakeholders to deliver meaningful insights
• Drive data governance, documentation and business-friendly data definitions
• Support self-service analytics and AI-powered reporting capabilities
• Improve data reliability, performance and automation across the analytics ecosystem

• Design and build ingestion pipelines across various sources
• Partner with data product owners to evolve semantic models
• Facilitate requirements-gathering conversations with stakeholders
• Lead and mentor a small engineering team

Senior Data Analytics Engineer

• Set and raise the bar on analytics engineering standards. Define the patterns, testing, and review practices that keep dbt models across the business consistent, documented, and trustworthy without you personally checking every one.
• Own the context layer. Bring the semantic layer (Omni) and the underlying domain models together into one place the business, human or AI, can query with confidence.
• Build the domain model from first principles. Take tem's data from raw source to a structured, trusted layer that powers commercial, financial, risk, and operational decisions, not just dashboards.
• Expand analytics engineering's reach across the business. Integrate new data sources, product and platform events, and the tools other departments run on, taking analytics engineering from a centralised function into new corners of the company.
• Help tem think bigger. As the business looks at big bets like international expansion and new ways of bringing its technology to other companies, help build a domain layer that's ready to go with it.
• Partner across the business, not just the data team. Work directly with engineers, product managers, and salespeople to understand what they're actually trying to achieve, then turn that into models that hold up under real use.

Staff Analytics Engineer

• Own the end-to-end design, implementation, and optimization of data pipelines and tables that support monitoring, reporting and analytics needs across RealityMine.
• Work closely with technical and non-technical stakeholders to define metrics, model datasets, and ensure accuracy, scalability, and performance in our data ecosystem.
• Design and build scalable data pipelines and tables to support analytics, monitoring, and reporting.
• Collaborate with stakeholders to define, validate, and standardise business metrics.
• Implement medallion-style data architecture, ensuring clear lineage, governance, and scalability.
• Optimise data pipelines for performance, reliability, and maintainability.
• Ensure data accuracy, completeness, and consistency across our data.
• Contribute to best practices for SQL, PySpark, and workflow scheduling (e.g. Airflow, Azkaban).

Analytics Engineer, Data Operations

• Create and own key data models in data warehouse
• Pioneer new tools and technologies for advanced analytical features
• Integrate fresh data sources for machine learning pipelines
• Collaborate with business and engineering teams for scalable architecture
• Manage ELT pipelines for reporting systems
• Guide product and engineering teams for analytics and ML requirements

• Provide subject matter expertise to help shape and guide the overall data modelling strategy across our data domains.
• Design and implement dbt models on Databricks to support a comprehensive and unified view of subscriptions and content data.
• Build data products, not just reports well-modelled, reusable data sets that power dashboards, self-serve querying and conversational analytics tools, with clear lineage back to source.
• Contribute to the ongoing evolution of our data platform by identifying and delivering improvements to data quality, coverage, and performance.
• Serve as a subject matter expert on data modelling, supporting analysts and stakeholders in understanding and using our data assets effectively.
• Design and implement scalable and maintainable dbt models on Databricks across our core data domains, including subscriptions and content.
• Create and maintain a suite of dbt tests to ensure data quality, consistency, and trustworthiness.
• Collaborate with data analysts, engineers, and business stakeholders to develop and maintain reporting marts that meet evolving analytical needs.
• Support the build-out of self-serve and conversational analytics capability, helping design data structures that natural language query tools can reliably and accurately query.
• Provide guidance on data modelling best practices, performance optimisation, and modular design in dbt.
• Participate in code reviews and knowledge-sharing sessions to uplift team capability.

• Work across our multidisciplinary consultancy team, with a primary focus on digital and data-driven analysis through automated processes
• Contribute analytical expertise to projects in digital strategy and digital planning
• Conduct robust modelling, data analysis, and research to support the digitisation of planning
• Build data and analytical pipelines (in Python) to automate key modelling workflows
• Develop robust, tested code that meets internal standards
• Prepare clear and compelling reports, technical outputs, and presentations for clients
• Collaborate with internal specialists across transport, energy, digital and systems modelling
• Support delivery of consultancy projects to time, quality and budget
• Respond flexibly to evolving project needs across the wider consultancy team

Data and Analytics Engineer – PhD Level

• Design and build data models with the support of AI-assisted workflows
• Own a growing share of the testing, data contracts, and observability
• Work with stakeholders to understand business needs and translate them into clear, well-scoped requirements
• Build and maintain robust data models and exposures in dbt, Snowflake, and Looker
• Read, debug, and critically review SQL (including AI-generated code)
• Document modelling decisions, trade-offs, and outcomes
• Help build and maintain the "trust infrastructure" for model reliability
• Create secure, efficient data shares for external partners
• Support business users in promoting a culture of data-driven decision-making

• Contributing to the data models that support credit decisions, origination, portfolio analysis, and investor reporting.
• Building and improving dbt models and transformations, guided by senior engineers and in close collaboration with analysts and stakeholders.
• Acting as a bridge between analysts, backend engineers, product teams, and the data platform team to help ensure data is modelled and used effectively.
• Identifying opportunities to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time.
• Supporting the scaling of our data infrastructure as the business grows.

Graduate Analytics Engineer

• Build and run the data foundation, dashboards and IBP data feed for the Top 10 EMEA Strategic Accounts.
• Data sourcing and integration. Build pipelines from cluster CRM, ERP, order-billing and pricing systems (and device-adherence data where used) into a common layer.
• Implement the governed Top-10 account hierarchy, survivorship rules and M&A re-parenting, per the Lead’s business rules.
• Build and maintain the dimensional single source of truth — fact tables across account-hierarchy, product, cluster, time and currency dimensions.
• Implement and validate the measures specified by the Lead (share of wallet, resupply / churn, baseline vs. ambition, etc.).
• Maintain data-quality monitoring, row-level security, GDPR controls, and audit.
• Build internal strategic-account views and customer-facing performance dashboards.
• Build the extract that maps the account demand plan and assumptions into the IBP structure.

Senior BI / Analytics Engineer, Strategic Accounts

• Design and maintain scalable dimensional models and high-performing Gold layers optimized for Power BI
• Define data grains, relationships, and historization strategies to translate business requirements into documented models
• Act as data gatekeeper by profiling datasets and implementing validation rules to ensure accuracy and consistency
• Collaborate with engineers to resolve quality issues and maintain integrity across the data pipeline
• Build optimized, reusable semantic layers to standardize metrics and eliminate duplication across reporting tools
• Partner with analysts to ensure data models are intuitive, performant, and aligned with end-user needs (Commercial, Finance, Operations etc)

Senior Analytics Engineer

• Owns the data modelling strategy, sets standards for data modelling, transformations, and governance.
• Leads the design and implementation of new features, such as the creation of a new star schema and other best practices to optimise data usability.
• Sets best practices and standards on how we use SQL & dbt / Python / Pyspark and/or other languages to solve business problems.
• Becomes an expert in the business subject area the team are working on, holding expertise on both the business requirements as well as the technical implementation.
• Delivers high quality code, provides technical mentorship, and ensures the team is building scalable, maintainable data models.
• Co-owns the backlog with the Engineering Manager and helps to break down technical requirements.

Senior Analytics Engineer – Tech Lead

• Supporting the delivery of new data models in a modern technology stack
• Collaborating with stakeholders from different business areas to understand their requirements and provide appropriate solutions
• Developing existing data models in line with product and engineering
• Upholding the data governance standards
• Supporting the BI and Analytics teams with key data sources required for their deliveries

• Building and improving the data models that support lending decisions, pricing, portfolio analysis, and investor reporting.
• Championing standards and contributing to the improvement of our analytics engineering culture.
• Supporting and collaborating with analysts at different technical levels, helping translate requirements into robust pipelines
• Helping triage and resolve issues that affect the analytics pipeline or reduce trust in downstream datasets, and contributing ideas to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time.

• Design and build analytics datasets and semantic layers for enterprise reporting
• Develop dashboards and analytical products used by business stakeholders
• Translate business questions into measurable metrics, dashboards and insights
• Design reusable analytics assets such as curated datasets, metrics frameworks and analytical models
• Implement CI/CD pipelines for analytics workflows
• Work with enterprise data platforms to deliver scalable analytics solutions
• Ensure strong data quality, governance and lineage practices
• Use AI-assisted tools and LLMs to accelerate analytics workflows, documentation and insight generation

Software Engineer – Analytics, Insights

Junior Analytics Engineer

• Support the building of robust data models downstream of backend services (mostly in BigQuery) that support internal reporting, machine learning as well as financial and regulatory use cases.
• Focus on optimisation of our Data Warehouse, spotting opportunities to reduce complexity and cost.
• Help define and manage best practices for our Data Warehouse. This may include payload design of source data, logical data modelling, implementation, metadata and testing standards.
• Set standards and ways of working with data across Monzo, working collaboratively with others to make it happen.
• Take established best practices and standards defined by the team, applying them within other areas of the business.
• Investigate and effectively work with colleagues from other disciplines to monitor and improve data quality within the warehouse.
• Contribute to prioritisation of data governance issues
• We all own and support the pipelines we contribute to, and on call support out of hours will be expected from time to time as part of this role

• Serving as a data architect for Monzo’s Operations data, contributing to the design and scalability of data models that measure the performance of our Operations product suite.
• Develop robust data models downstream of backend services, primarily in BigQuery, to support internal reporting, machine learning, large language models, as well as financial and regulatory use cases.
• Scoping, building and leading through others the re-architecture of entire domains across Operations (including data about our workforce and about demands of our customers)
• Be a key voice in shaping and maintaining best practices for our Data Warehouse, including source data payload design, logical data modelling, implementation, metadata, and testing standards.
• Collaboratively set standards and work with data across Monzo, fostering knowledge sharing and continuously improving data practices.
• Contribute to prioritising data governance issues, ensuring a comprehensive approach to data integrity and compliance.
• Being a key technical leader who’s able to champion central platform initiatives that aim to elevate the data standards across Operations and beyond.

Lead Analytics Engineer

• Build and maintain DBT models for cross-domain analytics use cases
• Create reusable facts, dimensions and marts for analytics consumption
• Support cross-domain data products such as Churn, LTV, Segmentation and Customer Intelligence
• Implement dbt tests, source freshness checks and reconciliation logic
• Translate business requirements into data models and metric logic
• Support data quality checks and model validation
• Document model definitions, business rules, assumptions and lineage
• Work closely with analytics and domain teams to ensure datasets are fit for reporting and decisioning

Analytics Engineer jobs in United Kingdom

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Analytics Engineer

Analytics Engineer

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Staff Analytics Engineer

Analytics Engineer, Data Operations

Staff Analytics Engineer

Analytics Engineer

Data and Analytics Engineer – PhD Level

Analytics Engineer

Graduate Analytics Engineer

Senior BI / Analytics Engineer, Strategic Accounts

Senior Analytics Engineer

Senior Analytics Engineer – Tech Lead

Analytics Engineer

Analytics Engineer

Software Engineer – Analytics, Insights

Junior Analytics Engineer

Senior Analytics Engineer

Lead Analytics Engineer

Data Analytics Engineer – DBT Modelling