IT engineer Data Engineering - Tech Lead job opportunity at Continental.

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Date2025-11-04T04:42:14.855Z bot
Continental IT engineer Data Engineering - Tech Lead
Experience: General
Pattern: Full-time
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degreeGeneral
loacation Electronic City Road, Bengaluru, Karnataka, India
loacation Electronic Cit..........India
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Job Description• Govern data acquisition and ingestion architecture for enterprise-wide data & analytics integration. • Establish reusable, scalable, and governed interfaces to all source systems across business domains. • Define standards and ensure engineering quality across ingestion pipelines and upstream data flows. • Act as escalation and design authority for ingestion, unstructured data, and advanced analytics integration points.• Oversees ingestion and integration design across 100+ data sources. • Supports 25+ data engineering and data science professionals with ingestion architecture and troubleshooting. • Interfaces with source system experts, data scientists, platform architects, and functional IT stakeholders. Main Tasks• Define ingestion frameworks for batch, streaming, and APIs. • Establish standards for JSON, CSV, XML, and unstructured data. • Govern manual uploads and exception workflows.• Collaborate with system owners to define interface specifications. • Manage schema evolution and API reliability. • Align ingestion architecture with platform and business requirements.• Collaborate on ingestion for data science and ML pipelines • Ensure robust API and event-driven integration for AI/LLM use cases. • Support complex acquisition scenarios with scalability requirements.• Review high-risk ingestion pipelines for performance and security. • Support data engineers on error handling, retries, and load orchestration. • Ensure ingestion aligns with governance and architecture.• Maintain ingestion libraries and templates. • Promote reuse and automation across engineering teams. • Lead best practices in TDD, code reviews, and pipeline testing.• Maintain architectural blueprints, templates, and best practices. • Publish design guidelines and coding standards. • Create re-usable architecture patterns for lakehouse environments.• Monitor usage and implement auto-scaling policies. • Analyze and optimize cluster configurations for cost-efficiency. • Provide cost transparency and usage reporting to stakeholders. 

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