Technical Specialist-Data Engg

Area(s) of responsibility

• 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.