Top Data Jobs in Belfast

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead enterprise-scale data science and AI initiatives, defining lifecycle best practices, architecture, governance, metadata frameworks, and MLOps practices. Design and deploy machine learning, statistical, optimization, and LLM solutions using cloud platforms. Lead multidisciplinary teams, translate business needs into analytical solutions, collaborate with engineers and stakeholders, and communicate insights to technical and executive audiences.

Coordinate group payroll processes across UK and rest-of-world, liaise with Global Mobility, P&C and outsourced providers, handle payroll queries and calculations, prepare journals for Oracle, update payroll procedures, and support meetings and implementation.

Conduct real-world data analyses using SAS or R and commercial claims and EHR databases. Build longitudinal study cohorts, define baseline and outcome variables, apply epidemiologic methods, perform statistical tests, regression and survival analyses, assess data quality, review protocols and statistical analysis plans, and program tables, figures, and listings. The role requires advanced biostatistics or epidemiology expertise and strong communication skills.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.

Hands-on role ensuring completeness, quality, and accuracy of product data for multi-region launches. Perform data validation, stewardship, QA, syndication (PIM), reporting, and stakeholder coordination to resolve discrepancies and track launch readiness.

Design, build, test and document secure batch and real-time data pipelines, perform logical/physical database design and data modelling, expand platform capabilities, analyse and resolve data processing issues, and ensure alignment with data architecture for downstream consumers.

Build, test, and document secure batch and real-time data pipelines for a government client; design logical and physical databases; extend platform capabilities; ensure data models align with architecture; perform data analysis and root-cause investigations to improve processing and availability for downstream consumers.

Serve as the bridge between business stakeholders and the data platform for corporate domains. Design conceptual, logical, and physical data models (SAP, SFDC, Procurement, Planning), develop dbt transformations/tests/documentation, map source-to-target lineage, enforce governance and naming standards, and partner with architects and BI teams to support self-service analytics and data integrity.

The Data Engineer will design and deploy production data pipelines, work with AWS technologies, and integrate data from various sources while collaborating with teams and stakeholders.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Designs and governs enterprise data architectures supporting analytics, reporting, operations, and AI initiatives. Responsibilities include data modeling, warehousing, integration pipelines, APIs, governance, quality, lineage, security, and architecture optimization. The role provides technical leadership and mentorship, guides cloud and big data technology decisions, develops proofs of concept and roadmaps, and supports client proposals and account growth. Collaboration with engineers, analysts, developers, and business stakeholders is central to delivering scalable, secure, high-performance data platforms.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Provides senior consulting expertise on data strategy, architecture, governance, transformation roadmaps, and scalable Microsoft Fabric solutions. Partners with clients, leads workshops, translates business needs into technical designs, supports pre-sales activities, develops proofs of concept, and contributes to proposals. Acts as a Microsoft Fabric subject matter expert while mentoring consultants and developers and managing multiple delivery priorities.

Leads data engineering teams building scalable, governed data products and platforms. Responsibilities include overseeing architecture, ingestion pipelines, orchestration, governance, security, access control, performance optimization, and Bronze/Silver/Gold data layers. The role manages delivery of analytics-ready datasets at very large scale, coaches engineers, and partners with analytics, business, and customer stakeholders to produce actionable operational and financial insights.

Support development and continuous improvement of data products for electricity transmission network planning and operations. Define roadmaps, manage backlogs, gather requirements, validate data quality and lineage, collaborate with stakeholders and delivery teams, enable analytics and governance, and measure product performance and adoption.

Lead business discovery and requirements analysis for data, reporting and analytics initiatives. Define processes, KPIs, data models, lineage and governance. Collaborate with analytics, data architects and delivery teams to elicit, validate and prioritise requirements, support testing, adoption and continuous improvement while managing stakeholders and multiple concurrent initiatives.

Lead enterprise data governance by defining frameworks, policies, and operating models. Manage data quality, metadata, catalogs, lineage, stewardship, compliance, and MDM. Collaborate with stakeholders, run governance councils, track KPIs, and support analytics, cloud, and AI initiatives.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

The Senior Data Engineer will enhance backend systems for a social media app by managing databases, optimizing recommendation algorithms, and ensuring accurate recommendations.

As a Lead Data Engineer, you will design and develop software for data processing, lead a team, ensure quality, and manage technical proposals.

Lead development and enhancement of Citi's trade cost analytics platform. Design trade and cost analytics, reconciliation processes, and fee calculations. Partner with front/middle office, operations, and technology to gather requirements, translate business needs into SDLC deliverables, and deliver reporting and platform improvements to increase transparency and accuracy.