Top Data Jobs in Belfast

Own fraud and scam analytics across channels, developing detection strategies, analyzing large datasets, identifying trends, and delivering actionable insights. Consult with stakeholders, influence risk-reduction decisions, represent the analytics team, and mentor junior colleagues. The role focuses on using SQL, SAS, or Python to create queries and technical solutions that reduce fraud losses while maintaining a positive customer experience.

Analyze Gecko browser engine telemetry across Desktop and Android, build leadership dashboards, identify telemetry gaps, develop frameworks to remove bot and hardware noise, and correlate performance metrics with user behavior. The role requires advanced SQL, statistical analysis, data visualization, browser component knowledge, and independent problem solving to translate engineering data into product strategy.

Manage the organization’s global data privacy program, ensuring compliance with GDPR, CCPA, HIPAA, UK and US privacy laws, and emerging AI regulations. Conduct audits, risk assessments, DPIAs, TIAs, LIAs, and vendor reviews; maintain processing records and privacy documentation; advise teams and clients; handle data subject requests and regulatory communications; support breach response and AI governance; and deliver privacy training and awareness programs.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Supports enterprise data governance and risk management across Citi by monitoring data issues, ownership, metrics, controls, findings, and corrective action plans. Coordinates with business, technology, regional governance, and risk teams; prepares regulatory and governance reporting; contributes to control framework assessments; and recommends improvements to data governance policies, standards, procedures, and risk taxonomies.

Leads reward data and analytics for global Pay Transparency Directive compliance. Responsibilities include pay-gap analysis, statistical assessment, data validation, statutory reporting, remediation modelling, Syndio administration, governance, audit documentation, and operational transition. Partners with Reward, Payroll, HR Technology, Legal, Privacy, Finance, and country stakeholders to ensure accurate, repeatable, compliant, and explainable reporting across multinational operations.

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.

Lead the technical direction of data engineering across client engagements. Design migration frameworks, streaming architectures, Lakehouse platforms, data integrity systems, and regulatory reporting pipelines. Provide architectural authority, review technical designs, select cloud services, mentor senior engineers, develop reusable delivery patterns, support hiring, and represent the consultancy externally. Shape proposals, scope technical engagements, present to senior stakeholders, and identify opportunities with existing clients.

Lead the technical direction of client data engagements, designing Microsoft Fabric and Azure data platforms, pipelines, models, testing, CI/CD, and operational practices. Manage and technically guide data engineers while remaining hands-on with complex challenges, incident response, and troubleshooting. Translate client requirements into technical roadmaps, collaborate with stakeholders, and ensure high-quality, commercially sound delivery using Python, PySpark, SQL, and modern data architecture patterns.

Designs, builds, and maintains production data pipelines using Microsoft Fabric, Python, and PySpark. Owns architecture, data modeling, CI/CD, reliability, troubleshooting, and technical decision-making. Collaborates with architects, product teams, and clients to translate requirements into robust solutions while mentoring junior engineers and improving engineering practices.

Graduate developers complete a three-month training program in Belfast covering Q programming and the kdb+ time-series database. They then work on financial data solutions for investment banks and hedge funds, potentially in global hubs including London, New York, Hong Kong, Singapore, and Toronto. The role requires a STEM degree, strong communication and problem-solving skills, teamwork, attention to detail, and willingness to relocate. Coding experience is beneficial but not required.

Graduate developer role featuring a three-month Belfast training program in Q programming and kdb+ time-series technology. Graduates will develop skills for financial data solutions supporting investment banks and hedge funds, with potential relocation to global financial hubs. Candidates need a STEM bachelor’s degree, strong communication and problem-solving abilities, teamwork, attention to detail, and willingness to relocate. Coding experience is beneficial but not required.

Build and enhance AWS QuickSight reporting for Customer Service, create reusable datasets, integrate business data sources, and establish consistent metrics and documentation. The role translates stakeholder needs into dashboards, automates manual reporting, validates data quality, investigates issues, and supports self-service analytics and knowledge transfer during a six-month engagement.

Own and build the company’s metadata-driven data layer as a hands-on principal architect. Design schema-flexible and versioned platforms with lineage, auditability, semantic mapping, and resilience to schema changes. Write production code, establish AI-assisted engineering practices with verification, coach engineers, and communicate architecture to technical and business stakeholders. Experience with regulated data, multi-tenant SaaS, semantic technologies, or clinical data standards is valuable.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Leads technical strategy and pre-sales engagement for hyperscale cloud, AI, and data center networking solutions. Partners with customers, hyperscalers, technology providers, sales teams, and product organizations to co-design architectures, shape go-to-market strategy, and influence product development. Requires deep expertise in routing, switching, SDN, network automation, virtualization, and hyperscale infrastructure, plus strong executive communication, RFP response, and relationship-building skills. Travel of up to 50%, including international travel, is required.

Drive Fluke’s business development and revenue growth across the European data centre ecosystem. Build strategic account plans, generate pipelines, win new logos, expand product adoption, and secure multi-site agreements with data centre operators, contractors, utilities, EPCs, and power-generation companies. Develop customer workflows, influence technical specifications, lead executive engagements, support channel partners, provide product feedback, and represent Fluke at industry events while serving as the company’s data centre subject matter expert.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Leads advanced data analysis, develops dashboards, KPIs, and analytical models, and translates business problems into data-driven solutions. Works with complex datasets using Databricks, SQL, Python, BI tools, and cloud data platforms. Partners with engineering and governance teams, promotes analytics standards, mentors analysts, and communicates insights to stakeholders. Supports strategic decisions through descriptive, predictive, and prescriptive analytics.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Analyze campaign and CRM data using SQL and BigQuery, reconcile data sources, investigate discrepancies, and build accurate Power BI dashboards. Present actionable marketing insights and recommendations to stakeholders, explain methodology and limitations, defend findings, and independently make routine analytical decisions while escalating significant blockers.

Leads the development of self-service data and AI platforms, analytical frameworks, AI-enabled workflows, and scalable data products. Combines on-chain and off-chain data to answer complex product and business questions, build trusted metrics, identify trends and risks, and support decision-making. Requires expert Dune and SQL skills, deep DeFi knowledge, and hands-on experience with Python, ClickHouse, Airflow, data engineering, and AI-enabled analytics.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.