Data Engineer designing and maintaining secure data platforms for advanced analytics in Security and Intelligence consulting. Collaborating with agile teams to deliver robust data engineering solutions.
Responsibilities
Design, build and maintain secure, scalable data platforms capable of processing large volumes of operational data.
Develop and operate robust data lakes and distributed data processing environments within secure or regulated settings.
Engineer high‑performance data ingestion pipelines for batch and streaming data from multiple sources, including sensor, geospatial, communications and operational systems.
Design and implement ETL/ELT workflows to transform, enrich and validate data for downstream analytics and machine learning use.
Build scalable, distributed data processing solutions using modern big data and parallel processing technologies.
Ensure data quality, reliability and performance through monitoring, validation and operational support mechanisms.
Collaborate closely with data scientists to enable analytics and AI/ML workloads, including feature pipelines and data preparation processes.
Optimise data storage and processing architectures to support high‑performance analytical environments.
Contribute to DevSecOps and cloud‑native delivery, including automated deployment, infrastructure provisioning and containerised environments.
Support the ongoing operation, observability and resilience of production data platforms.
Requirements
Experience in data engineering using modern programming languages such as Python, Java or Scala, and SQL
Hands‑on experience building and operating large‑scale data pipelines using big data and distributed processing technologies (e.g. Apache Spark, Hadoop)
Experience designing and maintaining data lakes, distributed storage platforms, and data pipeline orchestration tools
Experience working with cloud‑native data platforms, with strong expertise in AWS and native data services
Familiarity with Infrastructure as Code and modern cloud‑native data architectures
Experience working in DevSecOps environments, including Docker, Kubernetes, CI/CD pipelines, and observability/monitoring tooling
Ability to work directly with stakeholders to understand data requirements and translate them into robust, scalable data engineering solutions
Comfortable working in secure, regulated or constrained environments, delivering solutions within complex operational settings.
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