Top Data Engineer Jobs in Belfast

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.

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

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

Lead the design, development, and operation of Python-based data pipelines, ingestion workflows, and data models supporting search, retrieval, and AI-driven features. Integrate cloud data warehouses and business applications, manage deployment tooling and cloud infrastructure, and contribute to security and CI/CD practices. The role begins as a hands-on individual contributor and expands into mentoring engineers and guiding data architecture decisions in collaboration with the CTO.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Build and maintain scalable surveillance data pipelines for trade, communications, market, reference, and alert data. Implement data quality checks, reconciliation, monitoring, governance, lineage, auditability, retention, and recovery strategies. Develop AWS infrastructure with Terraform or CDK, write automated tests, investigate data anomalies, and collaborate with engineering, compliance, surveillance, and infrastructure stakeholders to meet regulatory requirements.

Leads enterprise data engineering strategy and delivery across Databricks, dbt, Azure, Python, and SQL. Defines architecture and engineering standards while driving modernization, automation, security, resilience, governance, and compliance. Influences stakeholders, coaches engineering teams, simplifies technology landscapes, and aligns data, cloud, analytics, and AI investments with business outcomes in a regulated environment.

Provides technical leadership for CDC data platforms and pipelines streaming data from DB2, VSAM, and Oracle into Kafka and HDFS. Responsibilities include designing scalable solutions, maintaining reliability and compliance, resolving production incidents, improving monitoring and SLAs, mentoring engineers, supporting prioritization, and managing stakeholder and vendor relationships. The role requires strong Java and Python skills, Kafka, Hadoop, Spark, Airflow, Terraform, CI/CD, containerization, and secure software development experience.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

Design, develop, and support scalable cloud-based data solutions across AWS and Azure. Build ETL/ELT pipelines, data integrations, migration processes, warehouses, data lakes, and lakehouses. Perform data profiling, quality assessments, validation, reconciliation, troubleshooting, and root-cause analysis. Create source-to-target mappings and technical documentation while supporting data governance, security, and compliance. Collaborate with technical and business stakeholders to deliver secure, high-quality data solutions.

Build and maintain automated cloud-based data solutions and scalable ETL pipelines. Design end-to-end data architectures, use Databricks, SQL, Python, and Spark, and ensure reliable, high-quality data delivery. Own complex backlog tasks, support code quality through reviews and pair programming, and experiment with innovative solutions. Help develop analytical tools that enable data-driven decisions, operational efficiency, and improved business performance.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

Lead the design, deployment, and operation of scalable data pipelines and cloud infrastructure for Kpler’s marine insurance initiative. Build containerized AWS services using Python, Docker, Kubernetes, Airflow, and Terraform; manage PostgreSQL and Kafka/Flink streaming systems; establish observability standards; provide technical direction; collaborate cross-functionally; and mentor engineers.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

Build and launch scalable data pipelines, products, APIs, and tools for proprietary market research. Develop big data processing systems, reusable software components, AI-ready data assets, cloud infrastructure, and governance controls. Partner with investment professionals, researchers, data scientists, and Compliance to deliver reliable, auditable, compliant solutions while optimizing system performance, cost, and reliability.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Lead large-scale technical initiatives and build scalable product functionality and infrastructure using TypeScript, React, and Node.js. Partner with product and design teams to develop privacy features for SaaS customers, make architectural tradeoffs, mentor engineers, support hiring, and establish engineering excellence across a growing organization.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Designs and delivers scalable data pipelines and Lakehouse architectures using Databricks, Spark, PySpark, and Delta Lake. The role covers ETL/ELT, data governance, security, quality, performance optimization, cloud integration, and CI/CD. It involves technical leadership across customer programmes, architectural decision-making, stakeholder collaboration, mentoring, and supporting reliable production data solutions.

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.