Sr Technical Lead-Data Engg

Birlasoft•INDIA - BENGALURU - AUTOMOTIVE, IN•On-site
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About the Role

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. <div class="