Data Engineer

We are currently seeking a Data Engineer to join our team.

The Data Engineer is responsible for designing, building, and managing assets and pipelines for data collection, storage, and analysis within Microsoft and Oracle environments. This role involves integrating large datasets from multiple sources to ensure that data is accessible, consistent, and of high quality for reporting/dashboards, insights, and analytics. Experience is required with Microsoft Azure cloud services, SQL Server, Oracle Database, and Oracle Cloud Infrastructure to develop scalable data solutions. Additionally, responsibilities include ensuring our data pipelines are robust and secure, supporting data-driven decision-making across the organization.

Here’s what would be included as a part of your typical day

1. Design and Develop Data Pipelines: Creates scalable data pipelines and ETL processes to extract, transform, and load (ETL) data from Microsoft and Oracle systems into centralized repositories. Use Fabric, Azure Data Factory and Oracle Data Integrator for efficient data extraction and transformation.
2. Data Storage & Management: Create and manage databases, data warehouses, and data lakes on Microsoft SQL Server/Azure and Oracle. Ensure high performance and reliability by designing, indexing, and partitioning relational databases. Monitor and tune database health to meet business needs.
3. Ensure Data Quality, Security, and Governance: Implement data quality controls, validation rules, and cleansing processes to maintain the integrity and accuracy of data within pipelines. Adhere to data governance standards and security protocols across both platforms, incorporating data masking or encryption where necessary, to ensure compliance with industry regulations and corporate policies.
4. Develop and Optimize Data Models: Design efficient data models and schemas for analytics and reporting on Azure and Oracle. Structure databases and data warehouses to support querying and BI reporting, and update architecture as needed.
5. Data Insights Support: Work with analytics teams to ensure that data is available and appropriately structured for BI tools. Provide curated datasets or views for reporting and assist in the creation of dashboards and reports, particularly with Microsoft Power BI, to convert raw data into actionable insights. 
6. Performance Monitoring & Troubleshooting: Monitor data pipeline and database performance and quickly resolve any issues. Use monitoring tools and logs to identify anomalies or bottlenecks. Optimize SQL queries, ETL jobs, and storage designs to improve throughput and reduce latency or cost.