Solution Design: Design and build re-usable, scalable data solutions and assets on a modern Lakehouse platform across a constantly evolving landscape of products and internal data teams.
Pipeline Development: Build and maintain robust, reliable pipelines using DBT, Apache Airflow, Snowflake, Databricks and AWS services.
Streaming & Real-Time: Develop real-time data pipelines using Apache Kafka and Apache Flink to support low-latency, event-driven use cases.
Data Integration: Connect to a wide variety of internal and external sources via multiple methods (SaaS solutions, APIs, direct connections, streaming) on cloud architecture.
Cloud Data Architecture: Architect and implement data solutions on AWS, Snowflake and Databricks, applying best practices for cloud-native data warehousing and cost-efficiency.
Lakehouse Architecture: Build and optimise open Lakehouse table formats such as Apache Iceberg, managing schema evolution, partitioning, and performance.
Modern Data Modelling: Apply modern data modelling practices that evolve with the business in an agile environment.
Performance & Cost: Write and optimise complex SQL and Spark workloads to keep solutions efficient in both time and cost.
Data quality and governance
Responsibilities
Solution Design: Design and build re-usable, scalable data solutions and assets on a modern Lakehouse platform across a constantly evolving landscape of products and internal data teams.
Pipeline Development: Build and maintain robust, reliable pipelines using DBT, Apache Airflow, Snowflake, Databricks and AWS services.
Streaming & Real-Time: Develop real-time data pipelines using Apache Kafka and Apache Flink to support low-latency, event-driven use cases.
Data Integration: Connect to a wide variety of internal and external sources via multiple methods (SaaS solutions, APIs, direct connections, streaming) on cloud architecture.
Cloud Data Architecture: Architect and implement data solutions on AWS, Snowflake and Databricks, applying best practices for cloud-native data warehousing and cost-efficiency.
Lakehouse Architecture: Build and optimise open Lakehouse table formats such as Apache Iceberg, managing schema evolution, partitioning, and performance.
Modern Data Modelling: Apply modern data modelling practices that evolve with the business in an agile environment.
Performance & Cost: Write and optimise complex SQL and Spark workloads to keep solutions efficient in both time and cost.
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