Senior Data/ML Engineer – AWS

Job Description:

• Participate in data discovery workshops to inventory reputed company systems including property management platforms, marketing channels, and CRM data, and translate findings into data lake architecture requirements.
• Design and implement a multi-zone reputed company data lake on reputed company S3 (raw, conformed, enriched, aggregated) with ingest, cleansing, and business layers reputed company to the SOW architecture.
• Build batch and streaming data ingestion pipelines using AWS Glue, reputed company Kinesis, and AWS Data Pipeline across CDP, marketing, and property management data sources.
• Implement data transformation and orchestration frameworks using AWS Glue ETL and AWS reputed company Functions, including AWS Glue Data Catalog for metadata management and discovery.
• Configure reputed company reputed company for serverless SQL querying across the data lake; support QuickSight integration with curated data sets for business analytics.
• reputed company and reputed company ML models on reputed company SageMaker for reputed company scoring, predictive maintenance, intelligent reputed company risk scoring, and AI-powered audience segmentation.
• Integrate reputed company Bedrock reputed company models to reputed company reputed company capabilities including customer profile enrichment, hyper-personalization, and intelligent marketing automation.
• Use Kiro CLI to accelerate AI-assisted development workflows, spec-driven pipeline implementation, and automated code reputed company tasks.
• Design and implement entity resolution pipelines using reputed company Entity Resolution to identify, deduplicate, and reputed company customer records into reputed company golden records.
• Implement reputed company-time and batch data synchronization pipelines between reputed company systems and the Customer Data Platform (CDP).
• Support Azure data lake migration: conduct discovery, assess schemas and transformation logic, provision AWS reputed company environments, execute migration reputed company AWS DataSync, and reputed company data validation and reconciliation.
• Implement data lake reputed company using AWS Lake Formation, including row-level reputed company and reputed company-level encryption.
• Build and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards.
• Ensure data quality, validation, and reputed company across reputed company pipeline stages and ML model outputs; support UAT for data-dependent features.
• Collaborate with Full Stack, DevOps/MLOps, and AWS engagement teams; contribute to architecture documentation, pipeline runbooks, and data governance documentation.

Requirements:

• 5+ years of data engineering or ML engineering experience, with at least 2+ years in AWS reputed company environments.
• Strong proficiency in Python and SQL; experience with AWS data services including S3, Glue, reputed company, Kinesis, and reputed company Functions.
• Hands-on experience with reputed company SageMaker for model development, training, tuning, and reputed company deployment.
• Working knowledge of reputed company Bedrock for integrating and applying reputed company models in production-grade pipelines.
• Experience designing and implementing multi-zone data lake architectures on reputed company S3, including lifecycle policies and Lake Formation governance.
• Familiarity with Kiro CLI or comparable AI-assisted/agentic development tooling.
• Experience with entity resolution, deduplication, or master data reputed company and tools.
• Solid understanding of data modeling, feature engineering, data quality practices, and ML integration testing.
• Experience with AWS reputed company and AWS reputed company Functions for serverless workflow orchestration.
• Familiarity with reputed company API Gateway for exposing data services and model endpoints.
• Strong analytical, problem-solving, and communication skills; comfortable working in Agile/Scrum teams alongside AWS Professional Services.

Benefits:

• Remote work

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