Role Summary:
We are seeking for a strategic Data Engineer focused on building and scaling enterprise data platforms and pipelines for an upcoming project. The position combines strong data engineering fundamentals with data strategy, architecture, and governance, while also driving AI-enabled innovation to improve decision-making and user outcomes.
It requires technical expertise, analytical thinking, and stakeholder collaboration across product, engineering, and business teams. The role also emphasizes a mindset of ownership, informed decision-making, and continuous learning to support evolving technologies and product growth.
Key Responsibilities:
This role spans end-to-end data engineering, AI, and platform delivery, focused on building scalable data systems that support product, analytics, and policy decisions.
You will design and maintain data pipelines, platforms, and models, deliver analytics and data products, and work closely with stakeholders to turn business needs into actionable insights. The role also includes developing and operationalising AI/ML and LLM-based solutions, supported by strong MLOps practices.
On the platform side, you’ll implement modern data architectures and DataOps practices (e.g., automation, streaming, data observability) to ensure high data quality, reliability, and performance, while managing technical debt.
You’ll also ensure governance, security, and compliance, proactively managing risks in data systems.
Beyond technical execution, the role requires ownership and leadership—driving projects independently, mentoring others, collaborating cross-functionally, and contributing to broader strategy, technology decisions, and organisational impact.
Requirements:
Experience & Education
- Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
- At least 5 years of experience in data engineering.
- Proven experience delivering data platforms or products in large-scale, enterprise environments.
Technical Skills
- Strong expertise in data modelling (OLTP, OLAP, dimensional modelling).
- Experience with cloud data platforms and tools (e.g. AWS, Databricks, Redshift, Data Lakes).
- Proficiency in big data technologies (e.g. Hadoop, Spark, Kafka, Flink).
- Strong programming skills in Python, Scala, or Java.
- Experience with CI/CD and DataOps practices (e.g. SHIP-HATS or equivalent).
- Expertise in data integration techniques (e.g. ETL/ELT, streaming, APIs).
AI & Machine Learning (Advantageous)
- Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.
- Familiarity with MLOps practices and cloud-based ML deployment.
- Awareness of responsible AI principles (fairness, robustness, safety).
