Data Engineer / BI Developer - DA&A

5 to 7 Years

India

This role focuses on designing and building scalable, cloud-native data pipelines and services while working closely with the Salesforce platform and engineering teams. The position will support the transition of legacy workflows (e.g., NiFi, Java APIs) to modern architectures leveraging GCP and Salesforce Data Cloud. It requires a strong data engineering foundation combined with working knowledge of Salesforce data models, integrations, and platform capabilities. Candidate must have relevant experience of 5 to 7 Years.

• Build and operate data pipelines in cloud environments (GCP preferred) with integration into Salesforce Data Cloud.
• Migrate and modernize legacy workflows to cloud-native solutions (e.g., Dataflow, Pub/Sub).
• Design and implement distributed data processing systems using technologies such as Spark and the Hadoop ecosystem.
• Develop and maintain integrations between Salesforce and external systems using APIs and event-driven patterns.
• Collaborate with Salesforce teams on data modeling, ingestion, and synchronization strategies.
• Apply working knowledge of Salesforce platform fundamentals (data model, security, APIs) in solution design.
• Develop backend services using Java and Spring Boot to support data and integration use cases.
• Ensure observability, reliability, and performance tuning of data systems and pipelines.
• Use containerization technologies such as Docker and Kubernetes and CI/CD practices for efficient deployments.
• Implement data engineering best practices to support Data Automation & AI and analytics use cases.
• Work closely with BI and analytics stakeholders to ensure data pipelines and models effectively support reporting and insight generation.
• Contribute to the modernization of data workflows to leverage GCP, Salesforce Data Cloud, and DataBricks where applicable.
• Maintain clear documentation for data flows, integrations, and operational procedures to support ongoing maintenance and knowledge sharing.

• Ability to design reliable, scalable data pipelines operating seamlessly across GCP and Salesforce Data Cloud.
• Capability to drive smooth migration of legacy systems with minimal disruption to downstream consumers.
• Strong focus on well-integrated data flows between Salesforce and external systems with clear ownership and observability.
• Commitment to improved operational efficiency through robust design, monitoring, and engineering best practices.
• Collaborative mindset to work effectively with Salesforce, data, and BI teams.
• Problem-solving skills to troubleshoot complex data integration and performance issues in distributed environments.

• Proven experience (5 to 7 years) as a Data Engineer or BI Developer in data analytics and data engineering environments.
• Strong hands-on experience with cloud platforms, preferably GCP, including services such as Dataflow and Pub/Sub.
• Experience designing and implementing distributed data processing systems using Spark and the Hadoop ecosystem.
• Working knowledge of Salesforce Data Cloud and Salesforce platform fundamentals, including data models, security, and APIs.
• Experience developing backend services using Java and Spring Boot for data and integration use cases.
• Proficiency in building and maintaining API-based and event-driven integrations between Salesforce and external systems.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Experience with CI/CD pipelines and modern software engineering practices for data solutions.
• Exposure to DataBricks and related data analytics tooling is preferred.
• Strong understanding of data modeling, data ingestion, and synchronization strategies for analytics and BI.
• Solid grasp of observability practices, including logging, monitoring, and alerting for data pipelines and services.
• Ability to write clean, maintainable code and follow best practices in version control and collaborative development.
• Good communication skills and the ability to work in cross-functional teams across data, engineering, and business stakeholders.