Data Engineer Palantir

Role Responsibilities

The nature of client engagements varies significantly, and responsibilities will depend on the project scope, client objectives, and your individual expertise. Typical activities may include:

• Designing, building, and maintaining high-quality data assets that enable analytics, operational decision-making, reporting, and advanced AI use cases.
• Developing robust and scalable data pipelines, integration processes, and data models to support complex business requirements and large-scale data environments.
• Applying innovative approaches to solve challenging data engineering problems, optimising data workflows, processing efficiency, and platform capabilities.
• Architecting enterprise-scale data solutions that support multiple stakeholder groups, enabling consistent access to trusted and actionable information.
• Defining and implementing logical and physical data models, governance frameworks, quality standards, and lineage processes to ensure reliable and compliant data management.
• Developing user-facing applications and digital solutions using a combination of low-code and software engineering approaches.
• Collaborating with multidisciplinary teams, including engineers, analysts, architects, and business stakeholders, to deliver effective data-driven outcomes.

Skills and Experience

All applicants must be willing and eligible to obtain the appropriate level of UK security clearance where required.

Candidates should possess hands-on experience across one or more relevant data and analytics technologies, with a strong background in data engineering, integration, and enterprise data platforms, including:

• Ingesting, transforming, enriching, and integrating data from a variety of structured, semi-structured, and unstructured sources.
• Designing, developing, and optimising ETL/ELT processes, data pipelines, and automated workflows.
• Building scalable, reliable, and high-performing data platforms and services.
• Implementing data governance practices, including metadata management, data quality controls, and lineage tracking.
• Supporting operational or mission-critical business processes through data-driven solutions and workflows.

We are particularly interested in candidates who combine strong technical capabilities with commercial awareness and can clearly communicate the value and impact of their work. Experience in the following areas would be advantageous:

• End-to-end delivery of data engineering solutions, including pipeline development, workflow orchestration, and data platform implementation.
• Working with diverse data types and processing patterns, including batch, streaming, real-time, and unstructured data.
• Solution architecture and systems-thinking capabilities, with experience designing scalable, resilient, and high-performance data ecosystems.
• Data modelling, information architecture, and database optimisation across logical and physical data structures.
• Monitoring, troubleshooting, and performance tuning of production data pipelines and processing environments.
• Strong software development practices, including writing modular, reusable, and maintainable code in languages such as Python, TypeScript, or similar.
• Knowledge of software development lifecycle (SDLC) principles, version control, testing frameworks, and CI/CD practices.
• Understanding of data security, governance, metadata management, master data management, and regulatory compliance requirements.
• Excellent communication and stakeholder management skills, with the ability to translate business needs into effective technical solutions and explain complex concepts to both technical and non-technical audiences.