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.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Develop and maintain customer-facing analytical reporting, data pipelines, and data models. Build Python applications, ensure data quality and consistency, collaborate with analytics engineers, and enable business teams to access actionable data. The role also involves cloud data warehouse optimization, infrastructure as code, software engineering best practices, and cross-functional product collaboration within an agile SaaS environment.

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.

Build and maintain reliable data pipelines using Python and SQL. Integrate REST APIs, databases, files, cloud storage, and third-party platforms; process and load data into warehouses for analytics and BI. Implement incremental and fault-tolerant loading, logging, monitoring, retries, error handling, and data quality checks. Work with ETL/ELT, Git, and potentially dbt, Spark, orchestration tools, cloud platforms, Docker, dimensional modeling, and BI datasets.

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.

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.

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.

Leads a Data Products team designing, developing, and evolving foundational data products for energy-market modeling, analytics, and client-facing systems. Owns data product quality, reliability, governance, schemas, versioning, metadata, and data contracts. Provides technical leadership, collaborates with subject matter experts and platform engineers, establishes engineering best practices, and supports agile delivery and continuous improvement.

Design, build and deploy production data pipelines and big-data architectures on AWS. Ingest, process and curate large structured and unstructured datasets, integrate multiple sources, write ETL scripts and SQL, and collaborate with cross-functional teams to deliver data warehouse and data lake solutions.

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 customer underwriting proof-of-concept projects using SQL, Python, and AWS to transform, validate, and deliver insurance risk data. Match address data to reference identifiers, investigate data-quality issues, document outputs, support underwriting data platforms, and automate manual workflows. Collaborate with technical and non-technical stakeholders, communicate project updates, and improve scalable data processes, quality, and customer experience.

Build and maintain automated cloud data solutions, scalable ETL pipelines, and data warehouses. Own complex backlog tasks, promote code quality through reviews and pair programming, and use experimentation to improve solutions. Support analytical tools that deliver actionable insights, operational efficiencies, and business performance metrics. Requires strong experience with Azure or other cloud data technologies, data warehousing, ETL, Databricks, SQL, Python, and Spark.

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.

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.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

Build and maintain cloud software for a Customer Data Platform, delivering complex features from design through production. Lead technical delivery, design scalable solutions, improve code quality, testing, observability, reliability, and security. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and roadmap discussions, and mentor engineers.

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.

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.

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.

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.