Data Engineer

At Jacobs, we challenge today to reinvent tomorrow by solving the world's most critical problems for thriving cities, resilient environments, mission-critical outcomes, operational advancement, scientific discovery and cutting-edge manufacturing, turning abstract ideas into realities that transform the world for good.

Your impact: This is a great time to join our Data & Digital team as we continue to scale our data platform and AI capabilities across critical infrastructure sectors. You’ll be working at the forefront of modern data engineering, helping design and deliver high-impact solutions that transform how organisations operate and make decisions. It’s an opportunity to work on complex, large-scale challenges, building production-grade data systems and AI-enabling platforms that deliver measurable outcomes across industries such as water, transport, and energy.

Day to Day:

• Design and build robust, scalable data platforms and pipelines on Azure and Databricks (batch and streaming).
• Develop high-quality data products using Python, SQL, Spark, and Delta Lake within modern lakehouse architectures.
• Create AI-enabling foundations including feature stores, ML-ready datasets, and automated model-serving workflows.
• Implement best practices in testing, observability, and monitoring (metrics, logging, lineage) to ensure platform reliability.
• Optimise cost, performance, and scalability across large-scale data workloads.
• Apply security-by-design principles, leveraging tools such as Unity Catalog for governance and access control.
• Automate engineering workflows using Infrastructure-as-Code (Terraform/Bicep) and CI/CD pipelines (Azure DevOps/GitHub Actions).
• Collaborate with clients to translate business challenges into technical solutions, facilitating workshops and aligning stakeholders.
• Continuously deliver value through iterative development, frequent releases, and outcome-driven delivery.
• Evaluate and implement modern data tooling and architectural patterns to improve platform capability.
• Contribute to reusable frameworks, templates, and scalable engineering practices.
• Communicate trade-offs, risks, and technical decisions clearly to both technical and non-technical audiences.

What’s In It For You:

• Work on large-scale, high-impact data and AI solutions across critical infrastructure industries.
• Build modern data platforms using cutting-edge technologies across Azure and Databricks.
• Be part of an engineering-led culture with strong investment in platforms, tooling, and innovation.
• Collaborate with highly skilled engineers, architects, and data professionals.
• Gain exposure to complex, real-world data challenges at scale.
• Develop deep expertise in cloud data engineering, AI enablement, and modern architecture patterns.
• Shape technical solutions that directly influence business outcomes.
• Opportunities to grow into principal engineering, architecture, or technical leadership paths.
• A global network of expertise and opportunity.
• A culture founded on safety, integrity, inclusion, and belonging.
• Flexible working arrangements that support your well-being and potential.
• Investment in your development, including certifications, learning time, and access to mentors.
• Opportunities to contribute to communities of practice and internal technical initiatives.
• Exposure to diverse projects across multiple industries and regions.
• Competitive benefits package including pension, holiday allowance, and additional perks.

Here’s what you’ll need:

• Azure (Cloud Platform): Experience building and scaling data solutions using services like ADLS Gen2, Data Factory or Synapse, and Event Hubs, with a strong understanding of security, networking, and enterprise-grade architecture.
• Databricks (Data Processing & Analytics): Hands-on experience delivering end-to-end pipelines in Databricks, using Spark and Delta Lake, with exposure to governance through Unity Catalog and ML workflows such as MLflow.
• Python & SQL (Core Engineering Skills): Strong capability in Python (PySpark) and SQL to develop, optimise, and maintain scalable, high-performing data pipelines and transformations.
• Modern Data Architecture (Lakehouse): Experience designing and working within lakehouse environments, applying data modelling approaches to create flexible, reusable, and high-performance data layers.
• CI/CD & DevOps Practices: Familiarity with Git, automated deployment pipelines using Azure DevOps or GitHub Actions, and Infrastructure-as-Code tools such as Terraform or Bicep to enable reliable, production-ready delivery.
• Data Quality, Observability & Governance: Experience implementing testing, monitoring, logging, and governance practices to ensure data is accurate, secure, and trusted across platforms.
• AI & Machine Learning Enablement: Exposure to supporting AI and machine learning use cases, including preparing feature datasets, enabling experimentation, and contributing to model deployment and lifecycle management.

As a disability confident employer, we will interview disabled candidates who best meet the criteria. We welcome applications from candidates who are seeking flexible working and from those who may not meet all the listed requirements for a role.