Job Overview At iCIMS, we're redefining how people connect with opportunity through intelligent, human-centred technology.

We're growing rapidly and are seeking Data Engineers at multiple levels of experience - from experienced to highly experienced professionals - to build the next generation of our Talent Cloud platform through scalable data pipelines, storage systems, and analytics infrastructure that power our data-driven decision-making and AI capabilities.

You'll design, build, and optimise data infrastructure that supports analytics, business intelligence, and product development, working with software engineers, data scientists, and product experts and providing technical leadership on projects in a culture that values innovation, ownership, and continuous learning.

Responsibilities Design, develop, and maintain scalable data pipelines to collect, process, and store data from multiple sources Build and optimise data infrastructure to support analytics, reporting, and AI/ML workloads Implement event sourcing and streaming architectures using platforms (e.g., Kafka, AWS Kinesis) for real-time data processing Apply data governance, security principles, and compliance frameworks to ensure data quality and regulatory adherence Collaborate with data scientists, software engineers, and product teams to deliver reliable data solutions Troubleshoot and resolve data-related issues whilst maintaining data quality and integrity Contribute to best practices, frameworks, and tools for data engineering excellence Drive improvements in data engineering practices and data architecture Mentor data engineers and provide technical leadership on projects Qualifications Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent professional experience) 4+ years of experience building data pipelines and systems Proficiency in Python; familiarity with Java Strong SQL skills and experience with relational and non-relational databases (e.g., SQL Server, PostgreSQL, MySQL, MongoDB)

Experience: with cloud platforms (AWS preferred) and data storage services (e.g., S3, Redshift)

Experience: with streaming platforms (e.g., Kafka, AWS Kinesis) and event-driven architectures Strong understanding of data modelling, warehousing, and schema design principles

Experience: with data transformation tools (e.g., dbt), BI platforms (e.g., Looker), and API development for data consumption Knowledge of version control (Git), CI/CD pipelines, and security principles for data systems (encryption, IAM, compliance frameworks)

Experience: with user behaviour tracking platforms (e.g., Snowplow, Google Analytics) is a plus Strong analytical and problem-solving skills with intellectual curiosity Strong communication and collaboration skills across both technical and non-technical teams Demonstrated experience in mentoring engineers, leading technical projects, or driving architectural decisions
Design, develop, and maintain scalable data pipelines to collect, process, and store data from multiple sources Build and optimise data infrastructure to support analytics, reporting, and AI/ML workloads Implement event sourcing and streaming architectures using platforms (e.g., Kafka, AWS Kinesis) for real-time data processing Apply data governance, security principles, and compliance frameworks to ensure data quality and regulatory adherence Collaborate with data scientists, software engineers, and product teams to deliver reliable data solutions Troubleshoot and resolve data-related issues whilst maintaining data quality and integrity Contribute to best practices, frameworks, and tools for data engineering excellence Drive improvements in data engineering practices and data architecture Mentor data engineers and provide technical leadership on projects
Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent professional experience) 4+ years of experience building data pipelines and systems Proficiency in Python; familiarity with Java Strong SQL skills and experience with relational and non-relational databases (e.g., SQL Server, PostgreSQL, MySQL, MongoDB)

Experience: with cloud platforms (AWS preferred) and data storage services (e.g., S3, Redshift)

Experience: with streaming platforms (e.g., Kafka, AWS Kinesis) and event-driven architectures Strong understanding of data modelling, warehousing, and schema design principles

Experience: with data transformation tools (e.g., dbt), BI platforms (e.g., Looker), and API development for data consumption Knowledge of version control (Git), CI/CD pipelines, and security principles for data systems (encryption, IAM, compliance frameworks)

Experience: with user behaviour tracking platforms (e.g., Snowplow, Google Analytics) is a plus Strong analytical and problem-solving skills with intellectual curiosity Strong communication and collaboration skills across both technical and non-technical teams Demonstrated experience in mentoring engineers, leading technical projects, or driving architectural decisions