Top Data Engineer Jobs in Belfast

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 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 support reliable data pipelines, ETL processes, integrations, and data assets for financial crime platforms. Ensure data quality, integrity, availability, reconciliation, and production support for transaction monitoring, customer risk assessment, sanctions screening, regulatory reporting, analytics, and audit remediation. Collaborate with stakeholders, analysts, engineers, and vendors to investigate root causes and improve enterprise data capabilities.

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.

The Data Engineer role involves implementing and optimizing SaaS data products, designing integration patterns, and providing technical leadership while collaborating with stakeholders.

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.

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.

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

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.

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, 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.

The Data Engineer will design and deploy production data pipelines, work with AWS technologies, and integrate data from various sources while collaborating with teams and stakeholders.

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.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages reliable Airflow orchestration, delivers data engineering solutions using Databricks, and contributes to GitHub-based collaborative development, code reviews, and engineering standards. Partners with data scientists, supports controlled feature-led delivery, investigates root causes, and develops practical solutions. The role also values adaptability, continuous learning, and exposure to MLOps concepts and tooling.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages Airflow orchestration and delivers data engineering solutions using Databricks. Collaborates through GitHub, code reviews, and shared engineering standards in feature-led environments. Partners with data scientists, owns delivery outcomes, performs root-cause analysis, and continuously improves pipeline reliability, observability, and maintainability. Exposure to MLOps concepts and tooling is desirable.

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.

Operate and architect large-scale distributed data infrastructure across Kafka, Hadoop HDFS, Ceph, SQL Server, bare-metal systems, and hybrid cloud environments. Responsibilities include capacity planning, reliability engineering, automation, observability, incident response, backup and recovery, platform tooling, and architecture decisions. The role requires expertise with petabyte-scale systems or billions of daily events, mentoring engineers, and shaping infrastructure standards across engineering organizations.

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, build, and support production ETL/ELT data pipelines across a cloud data stack. Partner with engineers and analysts to deliver reliable, well‑modeled data, ensure data quality and failure handling, tune SQL and warehouse objects, and handle semi‑structured formats like JSON and Parquet.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

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.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

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.