Top Data Engineer Jobs in Belfast

Designs, develops, tests, and supports reliable data engineering solutions for insurance data products. Responsibilities include data transfers, profiling, validation, quality management, issue resolution, data modeling, workflow management, code reviews, troubleshooting, and automation. The role collaborates with technical teams and business stakeholders to translate requirements into scalable, maintainable solutions while exploring AI-assisted engineering practices.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

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.

Leads electrical engineering and construction administration for data centre projects. Responsibilities include project planning, technical design, specifications, estimating, cost control, reporting, client presentations, site inspections, contractor coordination, and multidisciplinary team leadership. The role also mentors engineers, supports proposals, manages deadlines, and delivers electrical solutions across medium- to large-scale projects.

Lead mechanical engineering delivery for complex data centre projects across multidisciplinary and international teams. Provide technical leadership, collaborate across geographies, support permitting and construction methodologies, manage multiple projects, and contribute to AECOM’s data centre thought leadership and strategic growth. The role requires strong knowledge of building systems and regulations, client communication, digital design workflows, and data centre design and delivery.

Design and coordinate electrical services for data centre projects, managing delivery on small and mid-sized projects and assigned components of larger projects. Provide technical input, develop client solutions, prepare proposals and feasibility reports, attend coordination and site meetings, and liaise with clients, contractors, suppliers, and multidisciplinary teams. The role requires data centre or industrial facilities experience, professional accreditation, relevant electrical engineering qualifications, and willingness to travel.

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.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Design, develop, test, and implement Python applications, ETL pipelines, and real-time processing components for an enterprise reference data platform. Architect and optimize Oracle databases, SQL queries, and PL/SQL code while improving performance, scalability, and reliability. Collaborate with data stakeholders, support CI/CD and automated testing, troubleshoot production issues, and ensure governance compliance. Mentor junior developers and participate in Agile delivery.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

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.

Own the end-to-end delivery of applied machine learning products, from framing customer problems and building data pipelines to deploying, monitoring, and measuring model impact. Develop models for operational data using Python, SQL, dbt, Snowflake, and AWS; define evaluation and drift monitoring; conduct A/B tests or causal analyses; and partner with product and engineering teams to embed ML into customer workflows.

Own and develop Azure Databricks data-platform workstreams across data modelling, medallion architecture, Delta tables, data quality, infrastructure, CI/CD, DevOps, security, and governance. The role requires autonomous delivery from scoping through production, troubleshooting Azure infrastructure, managing Terraform and Azure DevOps, protecting PII, and engaging technical and business stakeholders globally.

Own and modernize end-to-end financial data pipelines, predictive models, and cloud infrastructure. Build real-time market-data ingestion, migrate services to AWS, rewrite PHP calculations in Python, and manage time-series and search databases. Implement data quality, monitoring, alerting, containerized deployments, CI/CD, and GitOps workflows. Collaborate with quantitative researchers and analysts while mentoring engineers and guiding architecture.

Build production data and reasoning systems for Codat’s AI-powered banking advisory platform. Responsibilities include developing semantic, signal, and reasoning layers; data pipelines, services, models, agents, and MCP servers; ensuring accurate LLM outputs through testing, observability, and evaluations; and owning systems from raw data through deployment and operation.

Own and enhance data intelligence products, BI solutions, dashboards, KPI frameworks, data governance, master data management, and workflow automation. Translate business objectives into actionable insights, scalable architectures, roadmaps, and measurable commercial outcomes. Analyze travel and meetings data, improve data integrity, document systems and processes, support client implementations, and collaborate with Sales, Finance, Technology, and leadership teams.

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

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.

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.

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.

Build and scale Phantom’s data infrastructure for analytics, experimentation, and decision-making. Responsibilities include designing high-volume data architecture, leading data quality and observability initiatives, developing scalable data models, building A/B testing infrastructure, partnering cross-functionally, and mentoring teams. The role requires expertise in Snowflake, dbt, SQL, Python, data architecture, data modeling, and experimentation systems, with blockchain experience preferred.

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.

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.

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.