Data Engineer

• Own and evolve core data pipelines, transformation logic, and curated datasets that support enterprise reporting and analytics.
• Design, build, and maintain scalable data models across warehouse / lakehouse environments, with a focus on reliability, clarity, and reuse.
• Implement strong data quality, validation, monitoring, and operational controls so critical data assets remain trusted and resilient.
• Integrate data from multiple source systems into well-structured datasets for analytics and reporting use cases.
• Work closely with analytics, BI, platform, and architecture colleagues to ensure downstream reporting and analytics sit on stable engineering foundations.
• Apply strong engineering discipline through CI/CD, version control, documentation, and repeatable delivery patterns.
• Improve performance, maintainability, and scalability of data pipelines and models as the platform grows.
• Help establish reusable patterns and standards for data engineering across the analytics function.
• Support the evolution of the analytics platform so it can serve not only reporting needs today, but broader analytics and digital use cases over time.

Senior Data Engineer

• Design, build, and maintain scalable, reliable data pipelines across GCP and AWS infrastructure, with BigQuery as our warehouse
• Own and evolve our Dagster orchestration layer, ensuring pipelines are observable, testable, and operationally robust
• Architect and implement ingestion patterns for diverse source systems, from SaaS APIs to acquired firm data with unstructured schemas
• Define and enforce data quality standards at the ingestion layer: completeness, freshness, lineage, security, privacy and schema contracts
• Build the technical playbook for onboarding acquired firms’ data into Lawhive’s canonical data model
• Design repeatable ELT patterns that handle conflicting schemas, messy legacy systems, and varying data quality, making firm onboarding a weeks-not-months process
• Partner with Analytics Engineering on the canonical Lawhive data model, ensuring upstream pipelines deliver clean, well-structured data
• Enabling access controls and privacy-preserving access to firm tenanted data
• Apply LLMs and AI tooling (Claude Code, Cursor) to data engineering tasks: entity resolution, schema mapping, automated data quality checks, and pipeline generation
• Partner with our AI/ML teams to build reliable data pipelines that feed model training and inference workflows
• Building scalable storage and processing solutions for various data and AI projects and products
• Proactively monitor and optimise BigQuery usage for query performance and cost efficiency as data volumes grow
• Evaluate and recommend tooling changes to keep the stack modern, efficient, and fit for AI-native workflows
• Work closely with the Analytics Engineer and Data Analysts to ensure the platform supports self-serve analytics and the dbt semantic layer
• Partner with Product and Engineering to instrument new product features and surface clean event data
• Contribute to documentation and runbooks that make the platform accessible and understandable across the team.

• Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data
• Build and optimize data solutions using Databricks, Apache Spark, and AWS
• Develop high-quality, maintainable code using Python and data engineering best practices
• Collaborate with product teams and business stakeholders to understand the meaning of the data you are building for
• Create and maintain data models, data quality and monitoring processes
• Improve performance, reliability, and scalability of existing data infrastructure
• Troubleshoot data issues and implement solutions to ensure data accuracy and availability

• Build and maintain a modern, scalable data platform that supports growth and decision-making.
• Ensure data is accurate, consistent, and trusted across the business.
• Improve speed, reliability, and automation of data pipelines and reporting workflows.
• Enable high-quality self-serve analytics by delivering well-modelled, well-documented data sets.
• Support digital performance and CRM insight through strong marketing data foundations.
• Design, implement, and maintain robust data pipelines across multiple systems.
• Ensure smooth, well-governed flow of data from source → warehouse → BI layers.
• Support end-to-end warehouse design and modelling as our stack grows.
• Integrate and manage a wide range of data sources within Snowflake.
• Build automated checks to monitor accuracy, completeness, and freshness.
• Identify opportunities to streamline pipelines, improve performance, and reduce cost.
• Work closely with teams across Growth, Finance, Ops, and Product to understand KPIs and reporting needs.
• Document architecture, pipelines, models, and workflows so everything is clear and easy to pick up.

• Build and maintain high quality solutions to serve ML models in various environments.
• Be involved in the creation of a new, bespoke AI platform that will support future AI initiatives.
• Maintain the company’s data and ML assets at the required quality levels.
• Follow the Agile methodologies implemented in the engineering team and keep to best practices in terms of code quality and design of processes.
• Assist with migrating existing ML projects to the new AI platform.
• Stay up to date with the latest technologies and methodologies and propose improvements to TMG infrastructure.
• Implement and maintain Google AI/ML products and the supporting infrastructure, for products such as Vertex AI, Gemini

AI Data Engineer

• Design, build, and maintain scalable, secure, and high performing data pipelines using Azure Data Factory, Synapse, Fabric Pipelines, or Databricks
• Integrate data from multiple systems into Azure Data Lake Storage or OneLake, ensuring consistency, reliability, and performance
• Develop and maintain ELT/ETL processes using Azure-native tools and Fabric capabilities, optimising for cost and performance
• Build and maintain Microsoft Fabric artefacts including lakehouses, warehouses, notebooks, dataflows, and semantic models
• Perform advanced data transformations using SQL, Python, Spark, or Fabric Data Engineering experiences to prepare data for analytics and reporting
• Manage and optimise data models used by Power BI, ensuring good performance, relationships, quality, and reusability
• Support enterprise BI development by preparing datasets, semantic models, and reusable components within Power BI and Fabric
• Apply data governance and security best practices by implementing Purview classification, lineage, access controls, and policies
• Conduct data quality checks, data validation routines, and issue resolution to ensure trustworthy and reliable datasets
• Work closely with cross-functional teams (data architects, analysts, data scientists, product teams) to understand requirements and translate them into robust data solutions
• Engage directly with clients and stakeholders to clarify data needs, communicate progress, and explain technical concepts in a clear, accessible way
• Take ownership of more complex engineering tasks and assist senior engineers with solution components requiring deeper technical insight
• Mentor junior engineers by providing guidance, performing code reviews, and sharing best practices in engineering standards and Azure environments
• Contribute to CI/CD processes, version control practices, and automated deployment pipelines for data solutions
• Support the creation of documentation for pipelines, datasets, data models, and solution designs
• Troubleshoot pipeline failures, performance issues, and data inconsistencies, ensuring the stability and reliability of production workloads
• Stay current with emerging tools and capabilities across Azure, Microsoft Fabric, Power BI, Purview, and Microsoft Foundry
• Identify opportunities to improve efficiency, automation, scalability, or maintainability across existing pipelines and solutions
• Contribute to internal knowledge sharing, best-practice development, and engineering accelerators or templates

• Marcura's data team is an AI-first data engineering organisation with significant ownership and opportunity to drive impact across the organisation and for our customers.
• We work across finance, commercial, and engineering to make sure everyone has access to accurate, timely and complete data within the constraints of their role.
• The data team own the data pipelines, modelling and warehousing required to support our stakeholders in the tools they use, be it AI models, dashboards, spreadsheets, or CRM tools.
• We are looking for a data engineer to own and developing the data pipelines and core data models for Marcura. If you're passionate about working as an AI-first data engineer and are eager to create solutions with significant impact, we'd love to hear from you.

• Design, build, and maintain data pipelines that ingest from a variety of sources – third-party APIs, operational databases, and file-based exports – primarily in Python on AWS.
• Own and evolve our data warehouse architecture and shape where it goes next – assessing and moving toward a cleaner, centralised warehouse or lakehouse that's well-structured, reliable, and managed as code.
• Build a fast, safe path from "new data needed" to "available to analysts and the business." Our current release flow is reliable but slow; you'll streamline testing, releases, and the overall experience of adding and changing models.
• Implement transformation tooling so analytics logic is version-controlled, tested, and reviewable. We use dbt and intend to keep building around it.
• Make it easy and safe for engineers, analysts, and product teams to access the data they need, with appropriate controls and auditability in place.
• Establish monitoring, alerting, and data quality checks across critical pipelines.
• Partner with analytics, engineering, and product teams to make their work faster, safer, and more reliable – including code review, mentorship on engineering practices, and improving developer experience.
• Contribute to our data security and compliance posture in line with healthcare regulatory standards (ISO 27001, GDPR).
• Help define our longer-term data platform strategy as the team grows.

• Design and implement robust data pipelines and ETL processes to support analytics, reporting, and ML model training
• Build and optimize scalable data storage and retrieval systems to handle high-volume, multi-market datasets
• Ensure data quality, consistency, and compliance with regulations such as GDPR
• Collaborate with data scientists and product teams to deliver reliable, well-structured datasets for experimentation and decision-making
• Implement monitoring and automation to improve pipeline efficiency and minimize downtime
• Contribute to the development of the overall data infrastructure roadmap for JD.com’s global operations

• Help shape our data strategy, principles, standards and roadmaps, and support their communication across the business.
• Work closely with the Data Platform team to ensure architecture is embedded effectively.
• Identify key master data sources, map data flows, and spot opportunities for reuse, quality improvements and reduction of duplication.
• Create and maintain core data architecture materials such as models, catalogues and documentation.
• Partner with Solution Architects to ensure transformation programmes are supported by the right data approaches.
• Lead the adoption of data modelling and cataloguing tools across the organisation.
• Manage risk and ensure compliance, building clear policies and processes to strengthen data quality and integrity.
• Improve visibility of enterprise data for reporting, analytics and data science by applying Bupa’s data architecture standards.
• Work with business teams to promote and embed Bupa‑wide data management practices.
• Communicate data governance expectations clearly and support accurate, compliant use of Bupa data.
• Promote strong data protection practices in partnership with our Privacy and Security teams.

Data Architect

• Develop and maintain data pipelines using Python, SQL, and Apache Airflow.
• Containerise data applications and workflows using Docker.
• Build and optimise data solutions using AWS services including:
• Redshift, OpenSearch, Lambda, Glue, Step Functions, and Batch.
• Collaborate with cross-functional teams to deliver robust, secure, and scalable data infrastructure.
• Manage version control using GitLab (or similar).
• Monitor and troubleshoot data pipeline performance and reliability.
• Contribute to documentation and process improvement initiatives.
• Ensure data quality, governance, and security best practices are followed.

AWS Data Engineer

• Design, build, and deploy large-scale distributed systems and high-throughput data pipelines using Go and cloud-native technologies.
• Lead system-wide architectural decisions, focusing on data flow, performance, and resilience. Actively contribute to the codebase with high quality code.
• Lead major technical initiatives, reduce technical debt and ensure the platform meets the reliability and scalability SLAs. Champion best engineering practices, code quality, testing and maintainability.
• Collaborate with product and engineering teams and R&D management to define the technical roadmap, review architecture and mentor junior engineers

Principal Data Engineer

• Collaborate closely with the platform provider and third-party vendors to manage and maintain the entire data ecosystem.
• Design, build, test, scale and maintain data pipelines from a various source systems and streams (internal, third party, cloud based, etc.), aligning with both business and technical needs.
• Ensure data quality through implementation of data quality frameworks.
• Work closely with data scientists/analysts to facilitate easily access to data for analysis. Engage with cross-functional teams across Sky Showtime to understand their data needs and provide tailored solutions.
• Operationalise and monitor data processing systems with a focus on security and compliance.
• Support functional teams within Sky Showtime by establishing new third-party and first-party data connections.
• Ensure data compliance with privacy and legal standards and requirements.

• Build and stabilise core data infrastructure: Design, build and maintain reliable, scalable pipelines and systems that ensure data flows correctly across the business and support critical operational and reporting workflows.
• Improve data reliability and observability: Own and improve the quality, resilience and maintainability of Attio’s broader data infrastructure — helping reduce operational risk and improve trust in the data underpinning the business.
• Shape Attio’s future data architecture: Partner closely with Product, Engineering and business stakeholders to define how our data platform should evolve over time, balancing immediate business needs with long-term scalability.
• Enable better business decision-making: Help create the foundational datasets, transformations and infrastructure required to support experimentation, analytics and operational reporting across the company.
• Operate as a senior technical leader: Bring clarity and direction within ambiguity — taking loosely defined problems, creating clear plans of execution, and driving outcomes with a high degree of ownership and autonomy.
• Help build the future data function: Contribute toward defining best practices, technical standards and longer-term hiring plans as Attio’s data capability matures and scales over time.

Data Engineering Lead

• Reporting to the Data Platform Lead, you will be a hands-on senior engineer responsible for building and maintaining ApprovalMax's enterprise data platform.
• Own the design and delivery of production-grade data pipelines, drive engineering quality across the data stack, and act as a technical mentor for the broader analytics team.
• Design, build, and maintain scalable ELT pipelines, ingestion processes, and transformation layers.
• Write production-grade Python for orchestration, custom ingestion, and data transformation logic.
• Investigate and resolve pipeline failures within agreed SLAs; lead root-cause analysis and implement durable fixes.
• Implement and maintain data quality frameworks across the platform; ensure critical data assets have explicit quality contracts.
• Partner with Product Engineering, RevOps, and Finance to define and maintain data contracts.

• Develop solutions using Azure Databricks and/or Fabric
• Implement Medallion Architecture (Bronze, Silver, Gold)
• Develop data warehouse and lakehouse solutions
• Support BI, analytics, and AI workloads
• Optimise performance, cost, and reliability
• Collaborate with technical and functional stakeholders

Lead Data Engineer

• Build and maintain ETL pipelines and code in cloud data platforms.
• Support ingestion activity and onboarding of new data sources.
• Work with the Databricks Data Intelligence Platform and develop Data Engineering workloads.
• Construct raw, refined and curated data layers; catalogue assets appropriately.
• Validate solutions against functional and non-functional requirements.
• Deliver datasets , transformations and performance-optimised data products.
• Improve processes, engineering patterns, and reusable tooling.
• Monitor and measure pipeline performance; support incident resolution.
• Ensure documentation meets acceptance standards and is approved centrally.
• Actively engage in Agile ceremonies and governance forums.

• Design and deliver a new data service within a large-scale organizational environment
• Shape data architecture and support data engineering activities
• Ensure data models and documentation meet organizational standards
• Collaborate closely with stakeholders to establish robust data structures and governance practices

• Build and operate the data platform
• Contribute to the design, delivery and operation of platform infrastructure using infrastructure-as-code, primarily Terraform.
• Improve CI/CD pipelines, deployment practices and platform tooling that enable data teams to self-serve safely.
• Work with security, compliance and data protection colleagues to reduce risk and improve data handling practices.
• Deliver reliable data products
• Build robust, scalable data solutions that support business needs such as CRM, SEO, financial reporting and personalisation.
• Stay close to the business context and apply software engineering practices to solve specific data problems.
• Improve monitoring, alerting, data quality checks and observability so issues are detected and understood quickly.
• Improve engineering workflows with AI and automation
• Identify repeated or high-friction engineering tasks and turn them into reliable automated or AI-assisted workflows.
• Help shape team practices for coding agents, prompts, local instructions, skills, hooks and tool integrations where they deliver real value.
• Balance speed with security, reviewability and maintainability when introducing AI-assisted delivery patterns.
• Enable the wider team
• Share practical learning through documentation, demos, pairing and show-and-tell sessions.
• Support more junior team members through coaching, code review and clear engineering standards.
• Keep an informed view of the fast-moving AI tooling landscape and recommend adoption where it solves real team problems.

• Collaborate with the Software Engineers, Risk Scientists, and Business SMEs and lead other Data Engineers to design, develop, integrate and test Data Product features on the product roadmaps to meet customer needs
• Design, develop, maintain and continuously enhance data processes that underpin critical regulatory and prudential change, delivering high-quality solutions while shaping how others approach the work through clear leadership and oversight
• Play a key role in building modern data products, pipelines and APIs that power current and future analytics and reporting
• Set the standard for engineering excellence—mentoring colleagues, offering constructive feedback and creating opportunities for shared learning and growth
• Work closely with the Product Owner and customers, help shape and prioritise the feature team backlog, applying strong problem-solving skills and sound technical judgement to assess options and define a clear, confident way forward

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