Top Data Jobs in Belfast

Senior Data Scientist owning end-to-end analytical problems: experiment design, predictive and inferential modeling, performance measurement, and cross-functional partnership to improve retail AI products. Duties include data acquisition, modeling, validation, auditing system outputs, building reusable statistical methods, and communicating findings to influence product and operations.

Data Contributors will research startups, analyze global data for StartupBlink's Global Startup Ecosystem Index, and compile accurate information for publication.

The Market Data Analyst will oversee data quality and operations for metals and minerals, monitor market trends, support clients, and improve data workflows using SQL and Python.

The role involves developing go-to-market strategies for VAST Data within the UK Defence & Secure Government sectors, building relationships with key stakeholders, identifying and closing strategic sales opportunities, and representing the company at industry events.

Own commercial growth for a strategically important enterprise account: build executive relationships, execute multi-year account plans, win new business, translate technical propositions into commercial cases, manage account marketing and governance, maintain pipeline and forecasting, and coordinate local GTM teams to drive predictable revenue growth.

Lead the design and development of data services, manage data reliability, create data models, and ensure data products support ML workflows.

Develop Front Office Trading software focusing on low latency components. Engage in end-to-end development, from design to implementation, mainly using C++.

Leads Swiss-AS AMOS configuration, aircraft data migration, validation, testing, and post-implementation support for aircraft induction and fleet transition projects. Manages data extraction, cleansing, mapping, transformation, reconciliation, reporting, and audit documentation. Coordinates with Engineering, CAMO, Maintenance Planning, Supply Chain, Quality, IT, OEMs, and lessors to ensure data integrity, regulatory compliance, and operational readiness. Supports project cutover, go-live, user training, and regulatory audits.

Lead design and delivery of scalable analytics, BI, forecasting, and AI-driven solutions for RevOps Sales Intelligence. Partner cross-functionally to improve data quality, governance, automation, and self-service analytics using cloud data platforms and modern ML/Generative AI techniques. Manage roadmap, stakeholder communication, and operationalize AI/analytics at enterprise scale.

The Platform Data Analyst will enhance visibility and performance optimization of Runware's media AI platform through analytics, monitoring, and reporting. Responsibilities include tracking performance, building dashboards, and collaborating with engineering and DevOps teams to improve observability and reliability.

The Data Engineer will build and maintain data infrastructure, optimize performance pipelines, and ensure efficient data ingestion and querying for analysis within a high-performance AI media platform.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

Lead the development of Smart Data Products within Actian's ecosystem, managing all stages of the product lifecycle and collaborating with cross-functional teams to drive innovation and adoption.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

Build and maintain production data pipelines and features in Python; own project lifecycle from design to reliable production; set technical direction for the Insights platform; improve engineering standards (testing, observability, data quality); apply AI across products and pipelines; help establish semantic/AI-ready data layers.

Lead end-to-end SAP S/4HANA data migration activities: define strategy and governance, manage mapping, cleansing, ETL and reconciliation, execute mock and production loads, resolve defects, coordinate with testing and business teams, and support cutover readiness.

Customer-facing technical leader owning long-term technical success and value realization for strategic enterprise accounts. Architect and lead implementations, migrations, and scalable cloud-native deployments; build joint success plans; monitor adoption and health; partner with Sales, Product, and Engineering; and provide technical guidance, reference architectures, and feedback to drive predictable expansion and customer outcomes. Travel to customer sites as needed.

Leads the Payments data strategy and partners with Product, Engineering, and Design to shape payment products, commercial optimization, customer growth, metrics, architecture, and operating models. Builds and scales a high-performing, full-stack data organization spanning data science, analytics engineering, machine learning engineering, and analytics. Serves as a trusted Payments Leadership partner, translating technical, product, and commercial perspectives into high-impact decisions.

Design, build, and support large-scale data systems and pipelines, ensuring reliability and scalability while collaborating with cross-functional teams to meet data requirements.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

Consult as part of a team building high-performance, cloud-native data platforms for financial markets. Design and implement scalable data pipelines, data models and analytical workloads using Python, SQL and ClickHouse. Deploy and optimise ClickHouse in client environments, work with distributed architectures, container orchestration and automation tools, and collaborate directly with client engineering teams.

Build production-grade AI agents, integrations, data pipelines, and infrastructure directly within strategic customer environments. Engineers rapidly navigate unfamiliar codebases and data stacks, own technical delivery through production deployment and handoff, communicate credibly with customer engineering teams, document and train users, and feed recurring engagement insights back to Product and Engineering. The role requires 30–50% travel during active engagements and emphasizes backend systems, agent integrations, real-world deployments, and maintainable customer outcomes.

Build, test, optimize, and maintain ELT pipelines supporting analytics, reporting, and data products. Deploy config-driven workflows to Databricks across a multi-tenant environment, extend data models, support CI/CD and ingestion projects, monitor pipeline health, troubleshoot failures, and collaborate with data and product teams to deliver reliable, well-tested data.