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Job Title – Data Engineering Consultant - Python Backend API | S&C

Management Level : 09 – Consultant

Location: PAN India

Must have skills: Python, FastAPI, RESTful API Development, API Testing & Automated Testing (Pytest), SQL & NoSQL Databases, Snowflake, Azure Cloud (Azure Web Apps), Git, Flask / Django REST Framework

Good to have skills: Spark / PySpark, Airflow, Docker / Kubernetes, GraphQL, Data Pipeline Design, MLflow, Pydantic, CI/CD, GCP / AWS

Job Summary:

As an Engineering Consultant, you will design, develop, and maintain scalable Python-based backend services and RESTful APIs that power analytical and data science products. You will serve as the engineering backbone for decision science teams, translating complex data models and analytical outputs into robust, production-grade APIs and pipelines on Azure Cloud. A strong emphasis of this role is building reliable, well-tested systems — ensuring APIs are validated end-to-end through automated testing, with rigorous coverage of functionality, error handling, and integration across backend services.

Roles & Responsibilities:

• Design, develop, and maintain scalable RESTful APIs and backend microservices using Python and FastAPI to serve data science models, scoring engines, and analytical outputs.
• Architect and implement efficient data access layers integrating relational databases (PostgreSQL, MySQL), NoSQL stores (MongoDB, Redis, DynamoDB), and Snowflake for high-throughput analytical workloads.
• Build and own a comprehensive automated testing suite using Pytest — covering unit tests, integration tests, and API contract tests — with effective use of mocking, fixtures, and parametrization to validate functionality, reliability, error handling, and cross-service integration.
• Maintain high test coverage standards and enforce testing best practices across the team, including validation of edge cases, failure modes, and API response consistency.
• Collaborate with data scientists and ML engineers to expose trained models and optimization algorithms via production-ready API endpoints deployed on Azure Web Apps and Azure Cloud services.
• Build and manage data ingestion pipelines and ETL workflows on Azure that feed downstream analytical systems, dashboards, and decision science products.
• Manage source code and collaborate across teams using Git, enforcing branching strategies, code review practices, and version control standards.
• Implement authentication, authorization, rate limiting, and API versioning to ensure security, reliability, and backward compatibility across services.
• Define and enforce API design standards, documentation practices (OpenAPI / Swagger), and code quality benchmarks across the team.
• Communicate technical architecture decisions clearly to both engineering and non-technical stakeholders including data science leads and business owners.
  
  

Professional & Technical Skills:

• Must Have Skills: Advanced Python programming with strong command of FastAPI, including async programming, dependency injection, middleware design, and request/response modelling using Pydantic.
• Hands-on experience with API testing and automated unit/integration testing using Pytest — including effective use of mocking techniques (unittest.mock, pytest-mock), test coverage tooling (pytest-cov), and thorough validation of API functionality, reliability, error handling, and integration across backend services.
• Solid knowledge of relational and NoSQL database systems — schema design, query optimization, indexing strategies, and ORM usage (SQLAlchemy); hands-on experience with Snowflake for analytical data access.
• Hands-on experience deploying and operating services on Azure Cloud, including Azure Web Apps, Azure Functions, Azure SQL, Blob Storage, and Azure DevOps pipelines.
• Proficiency in Git for source control — branching strategies (GitFlow), pull request workflows, and collaborative development practices.
• Strong understanding of software engineering principles — REST API design, SOLID principles, 12-factor app methodology, and microservices architecture.
• Good to Have Skills: Experience with big data technologies (PySpark, Apache Kafka, Airflow), ML model serving frameworks (MLflow, BentoML), GraphQL APIs, data clean room technologies, and visualization integration (Tableau, Power BI, Looker Studio).
  
  

Additional Information:

The ideal candidate sits at the intersection of software engineering and decision science — equally comfortable architecting a low-latency FastAPI service and discussing model inference pipelines with a data scientist. You bring a disciplined approach to testing and code quality, a genuine interest in making analytical outputs production-ready and impactful, and a mindset geared toward building systems that drive measurable business decisions.

This position is based at all PAN India offices.

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