Data Engineer

Responsibilities

• Design, build and maintain data pipelines, lakes and warehouses; develop ETL jobs, stored procedures, functions and packages to ensure robust data storage and high‑quality data delivery.
• Provide technical guidance and integration: own and implement ETL operating standards, guide infrastructure for analytics and AI use cases, and collaborate with backend developers and IT architecture teams for seamless integration.
• Collaborate with Product Owners to understand and meet data needs, translate product requirements into engineering plans and roadmaps, and gather stakeholder requirements to build architectures aligned with business goals.
• Promote a culture of process and data quality by implementing best practices for data management and quality assurance, and supporting bug fixing and performance optimisation.
• Advocate for agile methodologies and iterative development, collaborating cross‑functionally to deliver project outcomes.

• 3–6 years of experience in Data Engineering roles.
• Bachelor’s degree in a quantitative field (Mathematics, Engineering, Statistics, Science, Data Science/AI); advanced degrees preferred.
• Experience with Big Data platforms such as Databricks, Spark, Hive, Palantir Foundry and similar.
• Experience implementing AI solutions including OCR, Large Language Models etc.
• Experience with data modeling, design patterns and building highly scalable, secure analytical solutions.
• Proficiency in Python, SQL, PL/SQL, Spark and related languages.
• Strong analytical and problem‑solving skills; experience working in a tech/product environment is a plus.
• Behavioural competencies: high‑energy, ownership‑driven mindset; strong problem‑solving and analytical skills; excellent communication and collaboration abilities; comfortable navigating ambiguity and driving clarity; experience in agile/start‑up environments preferred; successful track record of leading complex projects is a plus.