Technical Specialist-Data Engg

Area(s) of responsibility

2. Senior Application Developer — Python Developer Grade  

Reports To  

Senior Technical Lead (Python)  

Program  

FP&A Reporting Automation & AI Forecasting  

Experience: 4–6 years · Location: [To be filled] · Engagement: Full-time  

Role Expectations  

• Build production-grade Python modules for Excel ingestion, template parsing, data transformation, validation, and consolidation of Data sets.  

• Develop backend services, APIs, and Azure Function-based pipelines to move data from Excel uploads (SharePoint/ADLS) into Database for downstream reporting.  

• Implement business rules, exception reporting logic, version control, and audit trails as per the technical design authored by the Technical Lead.  

• Write clean, testable, well-documented code with high unit test coverage; participate actively in peer code reviews.  

• Collaborate with the Data Scientist to operationalize ML models — building inference APIs, batch scoring jobs, and result-persistence layers.  

• Troubleshoot production issues, contribute to hypercare during go-live, and continuously improve pipeline resilience and performance. Must-Have Skills  

• Python (Strong): 4+ years hands-on with Python 3.10+, pandas, NumPy, and standard library; comfortable with OOP and modular design.  

• Excel Handling: Solid experience with openpyxl, pandas, xlrd/xlwings, and handling complex Excel structures (merged cells, formulas, pivot tables, named ranges).  

• APIs: Building Streamlit Applications and experience in consuming REST APIs using FastAPI or Flask  

• Version Control & DevOps: Git, branching strategies, pull requests, participation in CI/CD pipelines (Azure DevOps or GitHub Actions).  

• Testing: pytest, unit testing, and integration test practices.  

• Agile: Comfortable working in Scrum sprints, story estimation, JIRA/Azure Boards usage.  

Preferred / Good-to-Have Skills  

• Familiarity with Finance/FP&A concepts (P&L, forecasting, budgets) and Excel-heavy finance workflows.  
• Exposure to Streamlit / Plotly Dash for internal utility dashboards or admin UIs.  
• Basic understanding of ML model integration — calling scikit-learn models from Python services.