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Azure Developer

Hyperion Technologies
Email

Title: Azure Developer 
Location: Washington, DC

Hybrid Onsite: 4 Days onsite per week from Day1

Premium Skill:
Azure Cloud Services (PaaS and Iaas)
SAP APO, SAP Fiori, SAP BPC, S/4 Hana

Required Qualifications:
5+ years of experience in data engineering, with a strong focus on Azure Databricks.
Strong understanding of data modeling for planning and forecasting.
Experience with SAP BPC, including data structures, logic scripts, and planning process flows.
Hands-on experience with Azure services: ADLS Gen2, Data Factory, Synapse, Key Vault, etc.
Ability to translate legacy planning logic into modern, modular, and scalable data pipelines.
Experience working with cloud-based planning tools or their integration patterns.
Proficient in Python, SQL, and version control (e.g., Git).
Strong analytical and communication skills.

Preferred Qualifications:
Experience in finance, FP&A, or enterprise performance management (EPM) domain.
Prior involvement in BPC migration projects or cloud planning platform implementations.
Familiarity with cloud-based FP&A tools Key Responsibilities: - Analyze the current SAP BPC data models, processes, and integration points.
Design and implement scalable ETL/ELT pipelines in Azure Databricks to support data extraction, transformation, and delivery to the new planning platform.
Collaborate with SAP teams to extract actuals, plans, forecasts, and master data from SAP BPC (NetWeaver or MS version)
Translate BPC logic (scripts, transformations, allocations) into Databricks-based data models and logic.
Ensure accurate and timely data delivery from Databricks to the new cloud planning platform via APIs, flat files, or direct connectors.
Create reusable frameworks for data quality, lineage, and reconciliation.
Partner with solution architects and planning platform experts to ensure smooth integration and alignment.
Document technical solutions and support knowledge transfer to internal teams.
Ensure security, compliance, and performance, best practices are followed across the data stack.