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Job Summary
We are seeking a highly skilled and experienced Senior Data Engineer to join our team and contribute to the development and maintenance of our cutting-edge Azure Databricks platform for economic data. This platform is critical for our Monetary Analysis, Forecasting, and Modelling activities. The Senior Data Engineer will be responsible for building and optimising data pipelines, implementing data transformations, and ensuring data quality and reliability. This role requires a strong understanding of data engineering principles, big data technologies, cloud computing (specifically Azure), and experience working with large datasets.
Key Responsibilities
Data Pipeline Development & Optimisation
- Design, develop, and maintain robust and scalable data pipelines for ingesting, transforming, and loading data from various sources (e.g., APIs, databases, financial data providers) into the Azure Databricks platform.
- Optimise data pipelines for performance, efficiency, and cost-effectiveness.
- Implement data quality checks and validation rules within data pipelines.
Data Transformation & Processing
- Implement complex data transformations using Spark (PySpark or Scala) and other relevant technologies.
- Develop and maintain data processing logic for cleaning, enriching, and aggregating data.
- Ensure data consistency and accuracy throughout the data lifecycle.
Azure Databricks Implementation
- Work extensively with Azure Databricks Unity Catalog, including Delta Lake, Spark SQL, and other relevant services.
- Implement best practices for Databricks development and deployment.
- Optimise Databricks workloads for performance and cost.
- Program using the languages such as SQL, Python, R, YAML, and JavaScript.
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Data Integration
- Integrate data from various sources, including relational databases, APIs, and streaming data sources.
- Implement data integration patterns and best practices.
- Work with API developers to ensure seamless data exchange.
Data Quality & Governance
- Hands-on experience with Azure Purview for data quality and data governance.
- Implement data quality monitoring and alerting processes.
- Work with data governance teams to ensure compliance with data governance policies and standards.
- Implement data lineage tracking and metadata management processes.
Collaboration & Communication
- Collaborate closely with data scientists, economists, and other technical teams to understand data requirements and translate them into technical solutions.
- Communicate technical concepts effectively to both technical and non-technical audiences.
- Participate in code reviews and knowledge sharing sessions.
Automation & DevOps
- Implement automation for data pipeline deployments and other data engineering tasks.
- Work with DevOps teams to implement and build CI/CD pipelines for environmental deployments.
- Promote and implement DevOps best practices.
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Essential Skills & Experience
- 10+ years of experience in data engineering, with at least 3+ years of hands-on experience with Azure Databricks.
- Strong proficiency in Python and Spark (PySpark) or Scala.
- Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns.
- Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage, and Azure SQL Database.
- Experience working with large datasets and complex data pipelines.
- Experience with data architecture design and data pipeline optimization.
- Proven expertise with Databricks, including hands-on implementation experience and certifications.
- Experience with SQL and NoSQL databases.
- Experience with data quality and data governance processes.
- Experience with version control systems (e.g., Git).
- Experience with Agile development methodologies.
- Excellent communication, interpersonal, and problem-solving skills.
- Experience with streaming data technologies (e.g., Kafka, Azure Event Hubs).
- Experience with data visualisation tools (e.g., Tableau, Power BI).
- Experience with DevOps tools and practices (e.g., Azure DevOps, Jenkins, Docker, Kubernetes).
- Experience working in a financial services or economic data environment.
- Azure certifications related to data engineering (e.g., Azure Data Engineer Associate).
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