Role Description This is a critical role at the heart of our demand generation engine. You’ll make sure the data behind our campaigns is accurate, consistent, and usable; enabling our global teams to execute with confidence and precision. From maintaining clean CRM records to building reliable campaign lists and improving how data flows across systems, you’ll be the person who keeps everything connected and working as it should. - Data Quality & Governance - Own the accuracy and completeness of key data fields for marketing campaigns (sector, geography, hierarchy, contact mapping). - Maintain and improve data hygiene processes in Microsoft Dynamics 365 to reduce duplication and keep data clean over time. - Work with global teams to improve how data is entered, introducing clear standards, validation rules, and ownership. - CRM & Data Integration - Enrich and standardise CRM data using tools like ZoomInfo and Seamless. - Manage ongoing deduplication of accounts and contacts. - Ensure data is aligned across CRM, marketing, and BI platforms, improving how data flows between systems. - Campaign Data & Execution - Build and maintain clean campaign lists and segmentation in D365. - Source, validate, and upload data for campaigns and nurture programs. - Ensure consistent tagging and audience segmentation across markets so campaigns can run effectively. - Reporting & Insights - Maintain a regular data health report covering completeness, duplication, and enrichment. - Support campaign reporting by ensuring data is accurate and usable. - Identify and resolve data issues before they impact performance. Qualifications - Strong proficiency in Excel - confident working with large datasets, including data cleaning, comparisons, and transformations (VLOOKUP, pivot tables, etc.). - Familiarity with the data structures of CRM systems (Ideally Microsoft Dynamics 365). - Experience working with marketing or sales data, understanding how it supports campaigns and pipeline. - High degree of common sense and curiosity, with meticulous attention to detail. - Clear communication skills - able to document processes, explain issues, and drive consistency across teams. Benefits - Dynamic & collaborative team in a creative environment with exposure to global clients & colleagues. - Weekly webinars to support your development through our People Academy. - Annual TLC Wellness Week and programmes throughout the year. - TLC Culture Club - including seasonal social events, tasty lunches & more. - TLC Gives Back - volunteering opportunities, including off site visits and volunteering leave. - TLC Rise - supporting and empowering women into leadership roles. - 'Frankies' - Our very own awards ceremony where we walk down the TLC red carpet in our best outfits. - TLC Owner's Club - Everyone that is part of the TLC experience contributes to our success, which is why we all own a piece of TLC as part of our share holder scheme.

• Design, develop, and maintain end-to-end data pipelines using Databricks (Spark, PySpark, SQL) to ingest, transform, and curate structured and semi-structured data from authoritative DoD source systems. • Engineer and optimize data models, Delta tables, and curated datasets to support financial reporting, PPBE workflows, auditability, and mission analytics within Advana and downstream Foundry use cases. • Develop, maintain, and deploy Foundry pipelines, transforms, and data products, enabling traceability, lineage, and repeatable analytics workflows for DoD stakeholders. • Implement and manage CI/CD and version-controlled data engineering workflows using Git-based repositories and automated deployment patterns aligned with DoD security and ATO requirements. • Build and deploy cloud-native solutions in secure AWS and Azure environments, supporting IL4/IL5 architectures as applicable, and leveraging containerization where approved. • Partner with functional SMEs, financial analysts, platform engineers, and governance teams to ensure data pipelines conform to DoD financial data standards, business rules, and controls. • Implement robust data quality, reconciliation, validation, and monitoring capabilities to support financial accuracy, audit readiness, and confidence in reported metrics. • Support data integration, modernization, and migration efforts, including onboarding legacy financial systems and transitioning workloads to Databricks and Foundry within Advana. • Troubleshoot and resolve pipeline, performance, and data integrity issues in production environments, ensuring high availability and reliability in mission-critical systems. • Document data flows, engineering designs, and operational procedures to support compliance, knowledge transfer, and platform accreditation. • Stay current on emerging data engineering, analytics platform, and DevSecOps best practices relevant to DoD enterprise data environments.

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code