Group Data Engineer I

KEY ACCOUNTABILITIES

• Technical Leadership & Architecture:

• Define target architectures, engineering patterns and standards for batch/streaming integration, lakehouse design, data modelling, sharing and serving.

• Lead design reviews and technical decisions for complex or high-impact initiatives, ensuring scalability, security, resilience and reuse.

• Data Engineering & Platform Delivery:

• Design and build production-grade pipelines and reusable frameworks using SQL, Python, Spark and cloud data platform technologies.

• Establish reusable ingestion/transformation components, CI/CD and infrastructure-as-code to accelerate onboarding and reduce delivery risk.

• Reliability, Performance & Cost:

• Set standards for observability, SLAs, performance tuning, disaster recovery, incident prevention and root-cause resolution.

• Optimize compute, storage and workload design to improve platform performance, reliability and unit cost.

• Data Quality, Governance & Security:

• Embed automated data quality, lineage, metadata, access control, privacy and retention requirements into the engineering lifecycle.

• Partner with Governance and Security teams to ensure critical data products are trusted, auditable and compliant.

• Engineering Excellence & Automation:

• Drive automated testing, code quality, version control, deployment automation, coding standards and technical debt reduction.

• Evaluate emerging technologies, lead proofs of concept and convert proven capabilities into scalable enterprise standards.

• Collaboration & Mentoring:

• Mentor engineers, raise technical capability and provide hands-on support for complex troubleshooting and engineering decisions.

• Collaborate with product, analytics, AI/ML, platform and source-system teams to deliver reusable, trusted data capabilities.

QUALIFICATIONS, EXPERIENCE AND SKILLS

Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Technology or a related discipline; a Master's degree is desirable.
• Minimum of 7+ years of experience in data architecture, data engineering, or a similar role, with a strong focus on designing large-scale data platforms.

• • Advanced hands-on expertise in SQL, Python, Spark, distributed data processing and data modelling.

• • Strong experience with cloud data platforms (Azure, AWS or GCP); Databricks/lakehouse experience is preferred.

• • Deep knowledge of batch and streaming ingestion, CDC, orchestration, APIs, data lakes/warehouses and modern data architecture patterns.

• • Strong experience with Git, CI/CD, infrastructure-as-code, automated testing, observability and production engineering practices.

• • Working knowledge of data governance, security, privacy, lineage, metadata management and data quality controls.

• • Demonstrated ability to lead architecture/design reviews, resolve complex technical issues, mentor engineers and influence senior stakeholders.

Key Skills:

• Strong leadership, collaboration, and communication skills.
• Expertise in cloud platforms and services (Azure preferred).
• Proficiency in data pipeline orchestration tools (e.g., Apache Airflow, Azure Data Factory).
• Knowledge of containerization and microservices architecture.
• Familiarity with data visualization and BI tools (e.g., Power BI, Tableau).
• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Ability to think strategically while balancing business needs and technical solutions.
• Experience with Agile methodologies and working in a fast-paced, collaborative environment.

Desirable Qualifications:

• Certifications such as Microsoft Certified: Azure Solutions Architect Expert or Google Cloud Professional Data Engineer.
• Experience with machine learning and AI workloads on data platforms.
• Knowledge of DevOps practices and CI/CD for data pipelines.

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