Sr Technical Lead-Data Engg

Birlasoft; Karnataka; Hybride; INR 1 800 000 - 2 600 000; Plein temps

Résumé du poste

Birlasoft is seeking a Sr Technical Lead-Data Engg in Bengaluru to lead production-grade Python modules for Excel ingestion, data transformation, and consolidation. You will build backend services and Azure Function pipelines to move data from Excel uploads into a reporting database, and implement robust audit trails and error handling.

The role requires strong Python skills, Excel expertise, API development with FastAPI/Flask, and experience with version control and CI/CD.

Qualifications

• Strong Python 3.10+ proficiency with pandas and NumPy.
• Experience handling complex Excel structures (merged cells, formulas, pivots).
• Experience building/consuming REST APIs with FastAPI or Flask.
• Git-based version control and CI/CD workflows (Azure DevOps or GitHub Actions).
• Familiar with pytest and test-driven development practices.
• Agile mind-set; comfortable with Scrum, Jira/Azure Boards.

Responsabilités

• Build production-grade Python modules for Excel ingestion, template parsing, data transformation, validation, and consolidation of Data sets.
• Develop backend services and Azure Function-based pipelines to move data from Excel uploads into database for reporting.
• Implement business rules, exception reporting logic, and audit trails per technical design.
• Write clean, testable code with high unit test coverage and participate in peer code reviews.
• Collaborate with Data Scientist to operationalize ML models, building inference APIs and batch scoring jobs.
• Troubleshoot production issues, support go-live, and improve pipeline resilience and performance.

Connaissances

Python

Excel Handling

APIs

Version Control

DevOps

Testing

Agile

Outils

pandas

NumPy

openpyxl

xlrd/xlwings

FastAPI

Flask

Git

Azure DevOps

GitHub Actions

pytest

Streamlit

PlotlyDash

scikit-learn

Description du poste

Location: INDIA - BENGALURU - AUTOMOTIVE

Title: Sr Technical Lead-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 tohypercareduring 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 withopenpyxl, pandas,xlrd/xlwings, and handling complex Excel structures (merged cells, formulas, pivot tables, named ranges).
• APIs:BuildingStreamlitApplications and experience in consuming REST APIs usingFastAPIor 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 withFinance/FP&Aconcepts (P&L, forecasting, budgets) and Excel-heavy finance workflows.
• Exposure toStreamlit/PlotlyDashfor internal utility dashboards or admin UIs.
• Basic understanding ofML model integration— calling scikit-learn models from Python services.