Senior Data Engineer

Role Purpose

The Senior Data Engineer is a key technical leader within the Data Pillar. You are responsible for architecting, building, and maintaining the robust data pipelines and infrastructure that transform raw information into a strategic asset. You will act as a bridge between technical systems and business intelligence, ensuring that data is accurate, secure, and available for high-level decision-making.

Key Responsibilities

1. Data Architecture & Pipeline Engineering

• Design, develop, and optimize scalable ETL/ELT pipelines to integrate data from diverse internal and external sources.
• Architect and maintain a high-performance Data Warehouse/Data Lake environment.
• Implement automated monitoring and alerting systems to ensure pipeline reliability and data freshness.

2. Data Governance & Quality Assurance

• Establish and enforce data modeling standards (Star/Snowflake schema) to ensure reporting efficiency.
• Develop automated data validation checks to ensure Single Source of Truth integrity across the group.
• Ensure data privacy and security protocols are embedded into every stage of the data lifecycle.

3. Business Partnership & Collaboration

• Work closely with the Digital and Tech Ops pillars to understand source system structures and optimize data extraction.
• Translate complex business requirements from various departments into technical data models.
• Mentor junior data staff and provide technical guidance on best practices for data engineering.

4. Continuous Optimization

• Evaluate and implement emerging tools and technologies to improve data processing speed and reduce infrastructure costs.
• Optimize database performance through indexing, partitioning, and query tuning.

Required Qualifications

• Technical Mastery: 5+ years of experience in data engineering, with deep proficiency in SQL and at least one programming language (e.g., Python or Scala).
• Environment Experience: Proven experience managing cloud-based data stacks (e.g., Azure Data Factory, AWS Glue, or Google BigQuery).
• Modeling Expertise: Strong understanding of data warehousing concepts and dimensional modeling.
• Curiosity & Problem Solving: A proactive mindset with a focus on finding the root cause of data discrepancies.
• Communication: Ability to explain complex data architectures to non-technical stakeholders in clear, plain language.
• Language: Proficiency in English (Intermediate to Advanced level) for technical documentation and vendor collaboration.