Data Engineering Architect

As a Cloud-Native Data Engineer with 8-13 years of experience, your role will primarily focus on designing and implementing data engineering solutions on AWS. Your responsibilities will include: - Strong, hands-on expertise in AWS native data services such as S3, Glue, Step Functions, Lambda, Lake Formation, Athena, MSK, EMR, and SageMaker. - Designing and optimizing pipelines for both batch and streaming data ingestion. - Deep understanding of data mesh principles, enabling self-service platforms, and decentralized ingestion. - Advanced knowledge of schema enforcement, evolution, and validation using AWS Glue Schema Registry, JSON, and Avro. - Proficiency in modern ELT/ETL stack including Spark, dbt, AWS Glue, and Python for data transformation and modeling. - Designing and supporting vector stores, feature stores, and integrating with MLOps/data pipelines for AI/semantic search and RAG-type workloads. - Familiarity with metadata, catalog, and lineage solutions for implementing end-to-end lineage, auditability, and governance processes. - Design and implementation of data security measures such as row/column-level security, encryption, and role-based access using AuthN/AuthZ standards. - Experience with pipeline orchestration and monitoring in large-scale environments. - API design for both batch and real-time data delivery for AI, reporting, and BI consumption. Qualifications required for this role: - 8-13 years of experience in data engineering with a focus on cloud-native solutions on AWS. - Strong expertise in AWS native data services and modern data engineering technologies. - Proficiency in data mesh principles, schema management, data transformation, AI/ML data enablement, metadata/catalog/lineage, security/compliance/data governance, orchestration/observability, and APIs/integration. This role will offer you the opportunity to work with cutting-edge technologies in a dynamic and challenging environment.,