Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Responsibilities

• Lead Data Engineering teams and deliver large-scale data transformation programs.
• Manage stakeholders across Business, Finance, and Technology functions.
• Work within Agile and DevOps environments, including CI/CD practices and automation.
• Design, implement, and manage cloud-native data platforms on AWS or Azure.
• Translate complex technical concepts into clear business value and outcomes.
• Influence decision-making and drive data strategy initiatives.

Requirements

• Strong experience in Snowflake architecture and enterprise data platforms.
• Hands-on expertise with DBT (Cloud/Core).
• Hands-on expertise with data ingestion tools such as Fivetran, HVR, or similar technologies.
• Advanced SQL and data modelling techniques.
• Experience designing, implementing, and managing cloud-native data platforms on AWS or Azure.
• Strong understanding of modern data warehousing patterns, including Data Vault, Kimball Methodology, and Lakehouse Architecture.
• Proven experience leading Data Engineering teams and delivering large-scale data transformation programs.
• Strong stakeholder management skills across Business, Finance, and Technology functions.
• Experience working within Agile and DevOps environments, including CI/CD practices and automation.
• Experience within Financial Services or Insurance data environments is highly preferred.
• Understanding of financial data models and reporting structures, including Profit & Loss (P&L), Regulatory Reporting, and Enterprise Reporting Datasets.
• Excellent communication and stakeholder engagement capabilities.
• Ability to translate complex technical concepts into clear business value and outcomes.
• Strong analytical and problem-solving mindset with a focus on continuous improvement.
• Ability to influence decision-making and drive data strategy initiatives.

Skills

• Snowflake architecture
• Enterprise data platforms
• DBT (Cloud/Core)
• Data ingestion tools (Fivetran, HVR, or similar)
• Advanced SQL
• Data modelling techniques
• Cloud-native data platforms (AWS or Azure)
• Modern data warehousing patterns (Data Vault, Kimball Methodology, Lakehouse Architecture)
• Agile methodologies
• DevOps practices
• CI/CD practices
• Automation
• Financial Services data environments
• Insurance data environments
• Financial data models
• Reporting structures (P&L, Regulatory Reporting, Enterprise Reporting Datasets)
• Communication
• Stakeholder engagement
• Analytical skills
• Problem-solving
• Continuous improvement
• Decision-making influence
• Data strategy