Senior Data Engineer

Responsibilities

• Data Platform Engineering: Architect and maintain robust, scalable and secure data infrastructure on Databricks, covering both streaming and batch workloads.
• Data Pipeline Development: Design, develop and maintain data pipelines, primarily in Python and Spark, to automate ingestion and transformation across internal systems, external providers and on-chain sources.
• Data Modelling: Design and maintain dimensional data models and transformation layers in dbt, with tests and documented contracts, so metrics are consistent and reusable across the company.
• Data Lake Management: Oversee the data lake and lakehouse layers, ensuring efficient storage, effective partitioning, high data quality, and monitoring and alerting that surfaces issues early.
• Integration and Customisation: Integrate Databricks with a wide range of data sources, including change data capture from operational databases, third-party APIs and blockchain data, and adapt data flows to specific business needs.
• Performance, Scalability and Cost: Optimise pipelines and storage for performance, reliability and cost efficiency at scale.
• Data Governance and Security: Apply best practices for governance, security and compliance in cloud and Databricks environments, including access control, encryption and monitoring.
• Collaboration and Documentation: Work closely with platform engineers, data analysts and other stakeholders to understand data requirements, and document infrastructure, models and best practices.

Requirements

• Experience in Data Engineering: 3+ years as a Data Engineer, with hands-on ownership of production pipelines and lakehouse or data warehouse architecture.
• Databricks and Spark: Strong practical experience with Databricks, Delta Lake and Spark, including both streaming and batch processing, and the judgement to choose between them.
• Data Modelling: Solid understanding of dimensional modelling, slowly changing dimensions and data warehouse design, with the ability to define and defend the grain of the models you build.
• Transformation Frameworks: Experience with dbt or an equivalent framework for managing transformations, testing and lineage.
• Cloud Proficiency: Strong cloud fundamentals, including identity and access management, object storage, networking and infrastructure as code. We run on AWS; deep experience with Azure or GCP transfers well.
• Proficiency in Python and SQL: Comfortable writing production transformations as well as services and connectors.
• Data Quality and Governance: Experience implementing testing, monitoring and governance practices in cloud data environments.
• Nice to have:

- Experience with change data capture and replication from operational databases - Familiarity with blockchain or on-chain data - Infrastructure as code, ideally Terraform, and CI/CD for data workloads - Experience with BI and semantic layers such as Holistics, Looker or similar - Containerisation and orchestration (Docker, Kubernetes)