:

- Own and govern the data ingestion and integration architecture for the enterprise data platform.
- Define and evolve scalable, reusable, and production-grade data interfaces across all source systems and business domains.
- Provide technical leadership and design authority for all data platform ingestion and integration components, ensuring engineering quality, scalability, and reliability.
- Establish and enforce software engineering and architecture standards across data platform development (CI/CD, testing, observability, design patterns).
- Act as the primary escalation point and technical authority for complex ingestion, integration, and data platform challenges.
- Guide and support a distributed team of software engineers and data engineers in building production-grade ingestion services and platform components.
- Collaborate closely with platform architecture, cybersecurity, infrastructure, and business IT teams to ensure secure, compliant, and sustainable system design.
- Drive consistency and reuse across data platform patterns, frameworks, and engineering practices.

Main Tasks :

- Define and evolve ingestion frameworks for batch, streaming, API-based, and event-driven architectures.
- Establish and govern standards for structured and unstructured data (JSON, CSV, XML), including schema evolution and compatibility strategies.
- Design and review production-grade ingestion and integration patterns, ensuring fault tolerance, observability, and performance.
- Define and enforce API design standards, reliability patterns, and contract management.
- Lead design reviews for high-risk or complex ingestion pipelines, focusing on scalability, security, and maintainability.
- Support engineers in implementing robust error handling, retry mechanisms, orchestration, and monitoring.




- Collaborate with system owners to define interface specifications and integration strategies.
- Align ingestion architecture with overall platform design, governance, and business requirements.
- Enable ingestion patterns for advanced use cases, including data science, machine learning, and AI/LLM integrations.
- Maintain and evolve reusable libraries, templates, and framework components to accelerate development.
- Promote engineering best practices, including code reviews, testing strategies (unit, integration), and CI/CD pipelines.
- Define and maintain architecture blueprints, design guidelines, and engineering standards.
- Create reusable architecture patterns for lakehouse-based data platform.
- Provide guidance on scalability strategies, resource utilization, and performance optimization.
- Support cost transparency, usage optimization, and efficient resource consumption across the platform.

Qualifications :

- Degree in Computer Science or a related field.
- 6 to 10 years of experience in software engineering and data platform development, with a focus on large-scale, enterprise-grade systems.
- Proven track record of technical leadership and architectural ownership in distributed systems or data platforms.
- Strong hands-on experience with Scala / Java and Python in a production software engineering environment.
- Deep understanding of data integration patterns (APIs, CDC, streaming, event-driven architectures).
- Experience designing and governing reusable frameworks, platform components, and engineering standards.
- Solid knowledge of CI/CD, version control, automated testing, and observability practices.
- Experience with Microsoft Azure or other hyperscaler environments.
- Familiarity with lakehouse and data warehousing architectures; Databricks experience is a plus.
- Ability to guide and mentor engineers while collaborating across global teams and stakeholders.

📌 Contitech - Senior Software Engineer - Data Platform (India)
🏢 Continental Contitech
📍 India