Data Engineer

Job Details

Role Overview

We are seeking an exceptional Data Engineer to design, develop, and optimise intelligent, large-scale data systems that power advanced analytics, AI models, and cybersecurity intelligence within vQsystems’ innovation ecosystem.

This role demands a deep understanding of distributed data architectures, high-performance data pipelines, and scalable data storage strategies. You will work with cutting-edge technologies in a multi-cloud environment, collaborating with AI/ML, backend, and platform teams to deliver a real-time, insight-driven data infrastructure that fuels innovation across our enterprise.

If you are passionate about solving complex data problems, building resilient data architectures, and enabling AI-powered decision systems, this role offers a unique opportunity to shape the foundation of next-generation intelligent platforms.

Responsibilities

• Design, develop, and maintain scalable, real-time and batch data pipelines using tools like Apache Spark, Kafka, and Airflow.
• Build and optimise data lake and warehouse architectures on AWS, Azure, or GCP (Redshift, BigQuery, Snowflake, or Synapse).
• Implement ETL/ELT workflows that ensure high-quality, consistent, and secure data ingestion from multiple structured and unstructured sources.
• Collaborate with AI/ML engineers to design data pipelines optimised for machine learning models and continuous training.
• Develop and enforce data governance, lineage, and quality frameworks for enterprise-grade compliance and traceability.
• Implement monitoring, observability, and automation for all data flows to ensure reliability and minimal downtime.
• Work closely with software engineers and product teams to integrate real-time analytics and predictive insights into production systems.
• Continuously evaluate and integrate emerging data technologies to improve scalability, performance, and automation.

Requirements

• 4+ years of professional experience as a Data Engineer, preferably in complex, data-intensive environments.
• Strong proficiency in Python and SQL for data manipulation, transformation, and automation.
• Hands-on experience with big data technologies such as Apache Spark, Kafka, Hadoop, and Airflow.
• Proven expertise with cloud data platforms (AWS Glue, Redshift, GCP BigQuery, or Azure Synapse).
• Deep understanding of data modelling, warehousing, and lakehouse architectures.
• Experience in ETL/ELT design, data partitioning, and performance tuning.
• Familiarity with containerised and microservice-based architectures (Docker, Kubernetes).
• Exposure to AI/ML data workflows, including feature stores and model-serving pipelines, is a plus.
• Strong knowledge of data governance, compliance (GDPR), and security best practices.
• Excellent problem-solving skills, attention to detail, and ability to work collaboratively in a fast-paced, innovation-driven environment.

Benefits

• Competitive compensation and equity package.
• Hybrid or fully remote work flexibility.
• Opportunity to work on AI-integrated data infrastructure projects at enterprise scale.
• Access to modern data technologies and high-performance computing environments.
• Continuous professional learning.
• Inclusive, forward-thinking engineering culture that rewards innovation, precision, and long-term impact.