Required Skills & Experience
- 7+ years of experience in Data Engineering.
- Minimum 3 years of hands-on experience with Databricks.
- Strong hands-on experience with:
- PySpark
- Delta Lake
- Databricks Workflows
- Unity Catalog
- Strong SQL development and optimization skills.
- Strong experience in dimensional data modelling and star schemas.
- Hands-on experience with Slowly Changing Dimensions (SCD).
- Experience building production-grade data ingestion and transformation pipelines.
- Experience integrating multiple enterprise source systems, preferably ERP, planning and HR systems.
- Hands-on experience integrating Databricks with Microsoft Fabric.
- Good understanding of:
- Microsoft OneLake
- Fabric Lakehouses
- OneLake shortcuts
- Proven hands-on experience building LLM/AI agents using Python.
- Proven hands-on experience building MCP servers using Python in a real-world project.
- Understanding of secure and governed access for AI agents and tools.
- Strong understanding of data quality, reconciliation, lineage and data governance.
- Ability to work with business and technical stakeholders to translate requirements into data solutions.
Strongly Preferred Skills
- Experience in Finance, FP&A or financial data engineering.
- Strong understanding of:
- GL structures
- Budgets
- Forecasts
- Estimates
- Allocations
- Cost centres
- Financial planning scenarios
- Experience with TBM (Technology Business Management) classification or similar financial classification frameworks.
- Experience building AI-powered data engineering or ingestion solutions.
- Experience with agentic workflows for data mapping, classification and anomaly detection.
- Experience with additional LLM frameworks such as LangChain, LangGraph or similar.
- Experience with enterprise AI governance and secure AI/data access.
- Experience with Databricks data governance and lineage.
- Experience implementing automated data reconciliation and financial controls.
- Experience working with Microsoft Fabric-based enterprise analytics environments.
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