Sentinel is working with a global leader in technology, software, and services for the construction and engineering sector.

The business is developing its marketing data platform and is looking for an experienced Data Engineer to build new ingestion pipelines and improve existing data flows across digital and marketing data sources.

You’ll work within a modern AWS-based data environment, developing pipelines for external sources such as AppsFlyer, Didomi, and Google Search Console, while improving data quality, performance, and reliability.

This is a hands-on role requiring strong Python and SQL skills, with exposure across data ingestion, transformation, modelling, orchestration, and quality.

• Key responsibilitiesDesign, build, and maintain scalable and reliable data ingestion pipelines
• Develop ingestion pipelines for external APIs, marketing platforms, and digital data sources
• Modify and enhance existing Lambda functions, SQL queries, and data processing jobs
• Refactor existing pipelines to improve maintainability, scalability, and reliability
• Design analytical data models across silver and gold data layers
• Develop and optimise schemas using Apache Iceberg and modern data modelling principles
• Build automated data validation and quality checks across critical datasets
• Optimise SQL queries and data processing workloads for performance and cost efficiency
• Support pipeline orchestration, monitoring, failure recovery, and operational reliability
• Apply engineering best practices around automated testing, CI/CD, and Infrastructure as Code
• Maintain clear technical documentation covering transformations, code, and key engineering decisions
• Manage code through GitLab using appropriate version control and development practices
• Collaborate with engineers, product teams, and data stakeholders to understand requirements and deliver effective solutions

• What we're looking forStrong professional experience as a Data Engineer or within a comparable data engineering role
• Proven experience designing and developing scalable, reliable, and maintainable data pipelines
• Strong Python-based data engineering and development skills
• Strong SQL skills, including complex queries, analytical data modelling, and performance optimisation
• Strong data modelling and schema design experience
• Experience ingesting and processing data from external systems, APIs, or third-party platforms
• Experience working with cloud-based data platforms and modern data architectures
• Familiarity with pipeline orchestration tools such as Apache Airflow
• Experience with CI/CD, automated testing, and modern software engineering practices
• Good understanding of data quality, validation, and pipeline reliability
• Clear communication skills and the ability to work effectively with engineering, product, and data stakeholders
• Pragmatic approach with a strong sense of ownership

• Nice to haveHands-on AWS experience, particularly with Athena, Lambda, Glue, EventBridge, Aurora, and/or SageMaker
• Experience with Apache Iceberg and optimising Iceberg tables
• Experience with dbt Core or dbt Cloud for SQL transformations, modelling, testing, and documentation
• Experience optimising Athena queries or Spark workloads for performance and cost
• Understanding of data observability, monitoring, and automated failure recovery
• Experience applying Test-Driven Development within data engineering
• Exposure to AI-assisted coding practices
• Previous experience working with marketing or digital data sources such as AppsFlyer, Didomi, or Google Search Console
• Experience working within Agile/Scrum teams in a global organisation