Senior Developer

The Data Engineer will be responsible for developing and maintaining scalable data platforms, integrations, and AI-ready data foundations supporting HR Technology innovation initiatives. The role involves building data pipelines, integrations, vectorized knowledge repositories, AI-ready data models, and cloud-native data services to enable AI and GenAI solutions.

The individual will work closely with AI Developers, Solution Architects, HR Product Teams, and ecosystem partners including Microsoft, Google, Sarvam AI, and cloud platform providers to deliver robust data infrastructure for next-generation HR Technology solutions.

Data Engineering & Platform Development

• Design, build, and maintain enterprise-scale data pipelines.
• Develop batch and real-time data ingestion frameworks.
• Implement cloud-native data engineering solutions.

Data Integration & APIs

• Integrate HR systems, enterprise applications, SaaS platforms, and third-party services.
• Develop APIs and data connectors.
• Enable seamless data movement across platforms.

AI & GenAI Data Enablement

• Build AI-ready data environments.
• Create vectorized data repositories and knowledge stores.
• Support RAG, semantic search, and AI agent implementations.

Data Quality & Governance

• Implement data quality controls and monitoring frameworks.
• Support metadata management, lineage, security, and compliance requirements.
• Ensure privacy and responsible data usage practices.

Partner Collaboration

• Work with Microsoft, Google, Sarvam AI, and technology partners.
• Support AI engineering teams with data architecture and engineering expertise.
• Participate in solution design and enterprise architecture discussions.

Mandatory Skills

• Python
• SQL
• Data Modeling
• ETL / ELT Development
• Azure Data Factory
• Microsoft Fabric
• Azure Synapse
• BigQuery
• Data Integration & APIs
• REST Services
• Spark / PySpark
• Cloud Data Engineering
• Git & DevOps

Preferred Skills

• Databricks
• Snowflake
• Dataflow
• Event-Driven Architecture
• Kafka / PubSub
• Vector Databases
• Graph Databases
• Knowledge Graphs
• MLOps / DataOps
• SAP SuccessFactors Integrations
• AI/GenAI Platform Exposure
• Azure AI Foundry
• Vertex AI

Preferred Certifications

• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
• Microsoft Certified: Azure Fundamentals (AZ-900)
• Microsoft Certified: Azure AI Fundamentals (AI-900)
• Google Professional Data Engineer
• Google Cloud Associate Cloud Engineer
• Databricks Data Engineer Associate
• Databricks Data Engineer Professional
•  

---

Education & Experience

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, or related field.
• 2-5 years of Data Engineering experience.
• Hands-on experience in Azure and/or GCP data ecosystem.
• Experience in POCs, and building enterprise-scale data platforms and integrations.
• Prior exposure to AI/ML or GenAI initiatives will be an added advantage.