Core Responsibilities

• Architect and implement scalable data lake and lakehouse solutions on AWS for telemetry, analytics, and AI workloads.

• Design and manage secure data ingestion pipelines from edge devices and external systems into AWS S3 and structured storage.

• Develop and optimize ETL/ELT workflows using AWS Glue, Lambda, and Kinesis for structured, semi-structured, and unstructured data.

• Integrate LLM-based smart assistants with vector databases for contextual search and retrieval.

• Implement data partitioning, versioning, and metadata management strategies for high-volume datasets.

• Ensure data governance, audit logging, and compliance using Lake Formation, CloudTrail, and encryption mechanisms.

• Collaborate with platform, operations, and domain teams to curate domain-specific corpora and manage ingestion/index refresh jobs.

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Required Technical Skills
• AWS Core Services: S3, Lambda, DynamoDB, RDS (PostgreSQL), API Gateway, CloudFormation, CloudWatch.
• Data Engineering Tools: AWS Glue, Athena, Redshift, Kinesis, EventBridge, SQS, Lake Formation.
• Security & Compliance: SSE-KMS, IAM, VPC endpoints, S3 Object Lock, CloudTrail, encryption policies.
• IoT Integration: AWS IoT Core, Device Defender, fleet provisioning templates, telemetry routing.
• Containerization & Orchestration: Amazon EKS, Kubernetes, Argo CD.
• LLM & AI Pipelines: SageMaker, RAG architecture, prompt/response logging, evaluation metrics.
• Data Modeling & Cataloging: Data lake schema design, metadata tagging, data quality management.
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Preferred Experience
• Building data hubs for IoT platforms and predictive maintenance systems.
• Working with equipment metadata and technical documentation for industrial workflows.
• Experience with multi-modal data (structured logs, telemetry, vector embeddings).
• Familiarity with Generative AI integration in engineering or manufacturing contexts.
• Exposure to Infrastructure-as-Code (IaC) tools like Terraform, AIAC CLI for AWS provisioning.