Data Elephant

Data Engineer

• Lead discovery workshops to understand business priorities, source systems, data volumes, security requirements, and operational constraints
• Translate discovery findings into practical architecture and phased implementation plans
• Design Microsoft Fabric environments, including tenant and capacity considerations, workspace organization, development and production separation, and ownership and support responsibilities
• Define OneLake architecture, Lakehouse and Warehouse patterns, medallion layers, data organization, shortcuts, mirroring, and data movement
• Select ingestion, transformation, and orchestration approaches across Fabric Data Factory, Dataflow Gen2, Spark notebooks, and relevant Azure services
• Design data models and curated data products for reporting, Power BI semantic models, analytics, AI, and machine learning use cases
• Establish security and governance with client teams, including Microsoft Entra ID, access controls, data ownership, lineage, classification, and Microsoft Purview integration
• Set standards for Git, CI/CD, deployment, testing, monitoring, recovery, and performance and capacity management
• Validate key architecture decisions through hands-on implementation
• Lead technical delivery and architecture reviews
• Mentor engineers
• Work with pursuit and delivery teams on scope, estimates, dependencies, and implementation risks

Senior Data Architect – Fabric

• Build and maintain ingestion and orchestration pipelines using Fabric Data Factory and Azure Data Factory
• Integrate databases, APIs, files, and enterprise applications
• Develop ETL/ELT transformations using SQL, Python, PySpark, Fabric notebooks, and Dataflow Gen2
• Implement OneLake, Lakehouse, Warehouse, and Delta Lake solutions using medallion architecture where appropriate
• Develop full and incremental load patterns, change data capture, parameterized pipelines, and reusable ingestion frameworks
• Build dimensional models and curated datasets with analysts and business stakeholders
• Implement business rules, historical tracking, and source-to-target mappings
• Implement data quality checks, automated tests, reconciliation, and validation
• Handle schema changes, duplicate records, missing values, and late-arriving data
• Improve pipeline reliability through logging, monitoring, alerts, retries, dependency management, and safe reruns
• Troubleshoot failures and document operational runbooks
• Use Git and CI/CD to promote changes across environments
• Optimize SQL, Spark, and pipeline performance
• Implement agreed access controls
• Participate in code reviews and mentoring
• Work closely with architects, analysts, and clients from source analysis and development through testing, deployment, and production support

Azure Data Engineer – Fabric

• Design and build an executive-ready application on Databricks Apps
• Translate stakeholder needs into a working product
• Build BI-style visualizations and reporting components for an executive audience
• Contribute product thinking to a prioritized use case backlog
• Help scope future implementation phases
• Participate in client workshops and weekly status reviews
• Communicate technical progress to technical and non-technical stakeholders
• Apply AI/ML capabilities where relevant to enhance the application

Senior Data Architect – Fabric

Azure Data Engineer – Fabric

Software Engineer – Data Products, AI Applications