Backend/Integration Engineer - Python

Role Summary :

We are seeking an experienced Backend / Integration Engineer to design, develop, integrate, and support enterprise-scale data and application platforms. The ideal candidate will have strong expertise in Python development, ETL/ELT frameworks, PySpark, API engineering, cloud-based data integration, and enterprise application connectivity.

This role will be responsible for building scalable backend services, data pipelines, and integrations across business systems such as CRM, ERP, collaboration platforms, databases, cloud services, and AI-driven applications. The engineer will play a key role in enabling seamless data movement, data quality, governance, and near real-time business process integration.

Job Details

Number of post : 1

Office Location : Bangalore - bellandur, hybrid job 3 days from office - onsite client location

Job Type : Full Time

Gender : Male/Female

Industry : IT

Experience : 6-9 Years (6-10 years of software engineering and integration experience)

Interview process : Total 3 Technical rounds

Position :

Backend / Integration Engineer (P3 - Senior Developer)

Qualification :

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field.
• Strong expertise in Python development.
• Hands-on experience building enterprise integrations and APIs.
• Strong understanding of distributed systems and cloud architectures.
• Experience working in Agile development environments.

Key Responsibilities :

1. Enterprise Integration Engineering :

• Design and implement integrations across enterprise applications and platforms.
• Develop and maintain integrations with : Salesforce, SAP, SharePoint, Enterprise Databases, Data Warehouses, Cloud Storage Services, and External APIs and SaaS Platforms.
• Build event-driven and API-driven integration architectures.
• Support batch, real-time, and near real-time integration patterns.
• Monitor integration health and resolve operational issues.

2. API Development & Management :

• Design and develop RESTful APIs and integration services.
• Create secure authentication and authorization mechanisms.
• Develop API orchestration and service integration layers.
• Build reusable API frameworks and developer-friendly services.
• Support API documentation, versioning, and lifecycle management.

3. ETL / Data Pipeline Development :

• Design and build enterprise ETL/ELT pipelines.
• Develop scalable data ingestion, transformation, and enrichment processes.
• Automate data extraction from multiple enterprise systems.
• Build data validation, reconciliation, and auditing mechanisms.
• Optimize data movement, processing efficiency, and reliability.

4. Big Data & Data Engineering :

• Develop distributed data processing solutions using PySpark.
• Build large-scale data transformation pipelines.
• Support structured and unstructured data processing workloads.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Collaborate with data scientists, AI engineers, and analytics teams.

5. Database Engineering & Administration :

• Design and maintain relational and analytical data models.
• Develop database schemas, views, stored procedures, and performance optimization strategies.
• Manage enterprise databases including PostgreSQL, MySQL, Cloud Databases, and Data Warehouses.
• Responsibilities include query optimization, database performance tuning, data integrity management, backup and recovery support, and capacity planning.

6. Cloud Data Engineering :

• Build and operate data platforms on AWS and GCP.
• Develop cloud-native integration and data processing solutions.
• Implement scalable data architectures on cloud environments.
• Manage cloud data pipelines and infrastructure.
• AWS Services : AWS Glue, S3, Lambda, ECS/EKS, API Gateway, RDS.

7. Data Governance & Security :

• Implement enterprise data governance standards.
• Ensure compliance with data quality, retention, lineage, and security requirements.
• Support metadata management and data catalog initiatives.
• Ensure proper handling of sensitive and regulated information.
• Work closely with governance and security teams to maintain compliance standards.

8. DevOps & Deployment :

• Build and maintain CI/CD pipelines for backend services.
• Support automated deployments and release management processes.
• Implement monitoring, logging, and observability frameworks.
• Support production operations and incident management.

Key Skills & Requirements :

1. Programming & Backend Development :

• Python, SQL, Object-Oriented Programming, Microservices Architecture, REST APIs, Event-Driven Architecture.

2. Data Engineering :

• ETL / ELT Development, PySpark, Data Transformation, Data Quality Management, Data Validation Frameworks, Batch and Streaming Pipelines.

3. Enterprise Integration :

• Salesforce Integration, SAP Integration, SharePoint Integration, API Integration, SaaS Platform Integration, Message-Based Architectures.

4. Databases :

• PostgreSQL, MySQL, Relational Database Design, Query Optimization, Database Performance Tuning.

5. Cloud Technologies :

• AWS, AWS Glue, S3, Lambda, Cloud Storage.

6. DevOps :

• Git, CI/CD Pipelines, Docker, Kubernetes, Monitoring & Observability.

7. Governance & Security :

• Data Governance, Data Lineage, Metadata Management, Data Privacy, Security Best Practices, Compliance Frameworks.

Preferred Qualifications :

• Experience supporting AI/ML and GenAI data platforms.
• Experience building Retrieval-Augmented Generation (RAG) data pipelines.
• Exposure to vector databases and enterprise search platforms.
• Experience with workflow orchestration platforms.
• Knowledge of Master Data Management (MDM).
• Familiarity with data catalog and governance tools.

Joining :

Immediate / 15 Days.

Success Profile :

• Build scalable and reliable backend systems that power enterprise applications and AI platforms.
• Deliver robust integrations across business-critical systems including Salesforce, SAP, SharePoint, and databases.
• Develop high-quality ETL pipelines and data engineering solutions.
• Enable trusted, governed, and secure enterprise data ecosystems.
• Drive cloud-native modernization and automation initiatives.
• Partner effectively with product, AI, analytics, and business teams to deliver measurable business value.