Top Data Jobs in Belfast

Leads a team overseeing operational risks across technology, cyber, and data. Responsibilities include allocating resources, reviewing risk assessments, testing controls, monitoring threats, evolving risk policies and appetites, preparing executive reporting, and representing the oversight function at governance committees. The role acts as deputy to the Head of Operational Risk Oversight and provides independent challenge, strategic direction, team development, and assurance to senior management.

Own enterprise technical support cases from triage through resolution across Kubernetes, cloud infrastructure, ML platforms, authentication, data connectivity, and model deployment. Diagnose issues, write actionable Jira bug reports, create knowledge-base documentation, coordinate global case handoffs, and communicate clearly with customers. The role includes live troubleshooting sessions and participation in an EMEA weekend on-call rotation.

The Enterprise Account Executive will drive new enterprise revenue, engage with senior decision-makers, and execute growth strategies for Data and AI training solutions.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Lead delivery of a strategic risk technology transformation program for a hedge fund client. Responsibilities include managing project plans, milestones, dependencies, risks, issues, Jira workflows, JQL dashboards, executive reporting, stakeholder communications, release planning, system integrations, and migration from legacy technology to a modern risk platform. The role coordinates developers, quantitative teams, business stakeholders, and senior technology leadership while maintaining delivery governance and proactively resolving blockers.

Define the canonical data model and unified multi-tenant Lakehouse architecture across TRG Screen’s products. Establish data contracts, governance, master data management, semantic layers, security, ingestion, streaming, metadata, lineage, and quality standards. Guide technology selection and engineering execution while partnering with product and data leadership. The role also represents data architecture in design reviews and customer discussions and establishes architecture practices as the team grows.

Gather, clean, maintain, and analyze market data for financial institution clients. Build dashboards and reports, monitor data quality, identify discrepancies, support AI-driven automation, prepare client materials, present findings, and collaborate on data projects while developing expertise in financial markets and analytics tools.

As the Director of Data Science, you'll mentor teams, oversee projects, drive data-driven decisions, and implement AI and ML solutions while enhancing our data pipeline.

Build and deploy production machine learning models and fraud decisioning tools for e-commerce transactions. Partner with business teams to improve decisioning performance, automate manual processes, run experiments, and balance model complexity with performance. Write and review Python and SQL code, work with distributed data pipelines using Spark, Databricks, and GCP, communicate findings to technical and business audiences, and participate in an on-call weekend rotation.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

Design and deliver production-grade AI/ML solutions for Kainos Workday products, including LLM-driven features, model deployment, dashboards, and mentoring junior staff. Collaborate with engineering and clients to improve product functionality, scalability, and efficiency.

The Junior Data Analyst processes historical and live vessel telemetry, handles data queries, maintains data quality, and produces dashboards, reports, and actionable insights. Responsibilities include predictive maintenance analysis, investigating failure modes, correlating vessel data with external factors, supporting data acquisition and analysis pipelines, database management, cloud and edge processing, and documenting methodologies. The role requires Python scripting, data visualization, stakeholder communication, and rigorous analytical practices.

Design and deploy production data pipelines from ingestion through consumption using AWS cloud technologies. Build ETL pipelines, data warehouses, and data lakes; move and process large volumes of structured and unstructured data; integrate multiple sources; write scripts, extract data through APIs, and develop SQL queries. Collaborate with clients, stakeholders, product, design, and engineering teams using agile practices to deliver scalable digital solutions.

Lead design and delivery of production-grade AI/ML solutions for Kainos Workday products. Build predictive analytics, anomaly detection, generative AI features, and scalable MLOps pipelines. Integrate with Workday, mentor and line-manage a data science team, manage client engagements, and drive architectural and technical leadership across product initiatives.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Designs, builds, operates, and supports secure, governed data pipelines and services for reporting, integration, migration, and analytics. Responsibilities include ingestion, transformation, validation, publishing, data modeling, quality controls, lineage, metadata, access controls, migration, monitoring, troubleshooting, and documentation. The role collaborates with analysts, architects, developers, and business stakeholders across cloud data environments.

Leads engineering strategy, architecture, delivery, and productionisation for a secure cloud Data Intelligence Platform. Designs ingestion, streaming, transformation, lakehouse, serving, API, and feature-pipeline capabilities while establishing data quality, governance, lineage, observability, CI/CD, infrastructure-as-code, resilience, and FinOps standards. Coordinates analytics, ML, security, infrastructure, suppliers, and operations; manages technical priorities and risks; supports governance decisions; and mentors data engineers.

Embedded within the commercial team, the Staff Data Analyst will own trusted metrics for conversion, contracted volume, sales, margins, pipeline, and forecasting. Responsibilities include improving forecast accuracy, diagnosing CRM and transactional data quality issues, analyzing commercial opportunities and partner performance, identifying risks and trends, and translating complex findings into recommendations for senior stakeholders. The role also promotes metric adoption and self-service analytics across non-technical commercial teams.

Build and operate Python-based data ingestion, ETL, detection, and forensic artifact extraction pipelines for a mobile EDR platform. Productionize threat detection prototypes, optimize large-scale data storage and queries, deploy services on AWS, and establish monitoring, validation, fault tolerance, and data quality controls. Collaborate closely with security researchers to develop reliable tooling and scalable systems for identifying mobile spyware and other threats.

Leads reference data operations across multiple teams, managing complex issues, projects, resources, process improvements, risk controls, and escalations. Acts as a subject matter expert and advisor, influences senior stakeholders, supports operational strategy, coaches colleagues, and ensures compliance with applicable policies and regulations. The role requires extensive operational management experience, MCA framework knowledge, risk or issue management exposure, strong communication, and a bachelor's degree.

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 the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

Evaluate and annotate 3D meshes, GLB files, images, and short videos for model training and error resolution. Identify whether assets are artist-created or AI-generated, and detect geometry defects, topology errors, and scene inconsistencies. Requires hands-on 3D modeling experience, familiarity with Blender or Maya, strong mesh-topology knowledge, English proficiency, and a suitable computer with dedicated graphics capabilities.

Lead design, engineering and delivery of cloud-native, Databricks/AWS-based data services. Drive migration to Databricks, build scalable Spark/Scala pipelines, optimize performance, mentor teams, manage technical delivery, and enable AI/GenAI integrations while ensuring governance, security and regulatory compliance.

Build and evolve a strategic Databricks-based Research data platform. Design scalable pipelines and governed data products using Python, SQL, Spark, Azure, Delta Lake, and ADLS. Implement ingestion, transformation, data quality, reconciliation, lineage, monitoring, audit controls, CI/CD, and deployment practices. Partner with global stakeholders and engineering teams to deliver analytics, BI, and AI-ready data capabilities while coordinating offshore delivery resources in India.