Data Engineer, Analytics Engineering, VP

Role overview

Responsibilities

• Lead complex cross-domain data models and governed, trusted data products.
• Set data standards, governance frameworks, and analytics engineering best practices.
• Enable self-service analytics, reporting, AI, and advanced data products through canonical and semantic models.
• Establish DataOps practices including dbt, CI/CD, testing, lineage, and data quality.
• Mentor analytics engineers and data modellers; represent PBWM in enterprise architecture and governance forums.
• Automate pipelines, source data with appropriate tools, and develop streaming ingestion and transformation solutions.
• Support training and experimentation, and clarify platform costs against cost-saving and income targets.

Requirements and skills

• At least 15 years in Analytics Engineering, Data Architecture, Data Modelling, Data Platform Engineering, or related disciplines.
• Expertise in canonical and semantic modelling, data product design, metadata, lineage, governance, and product-oriented architectures.
• Expert SQL and advanced dbt experience, including models, macros, tests, documentation, lineage, CI/CD, and enterprise delivery.
• Extensive Snowflake experience across architecture, performance, cost, governance, security, and platform capabilities.
• Strong AWS analytics-platform knowledge including S3, Glue, Athena, Iceberg, IAM, security controls, and governance.
• Financial Services and PBWM experience or knowledge is advantageous; SageMaker Unified Studio and DAMA/DMBOK or similar are also noted as preferred or advantageous.

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