Top Data Engineer Jobs in Belfast

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.

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.

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.

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.

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.

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.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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.

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.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Design, build and maintain scalable, automated cloud data solutions and pipelines. Provide technical leadership to a small engineering squad, own complex backlog tasks, develop data frameworks and standards, and ensure code quality via reviews and pair programming using Databricks, Python, SQL and Spark.