Senior Data/ML Engineer – AWS

Job Description:

• Participate in data discovery workshops to inventory source systems including property management platforms, marketing channels, and CRM data, and translate findings into data lake architecture requirements.
• Design and implement a multi-zone enterprise data lake on Amazon S3 (raw, conformed, enriched, aggregated) with ingest, cleansing, and business layers aligned to the SOW architecture.
• Build batch and streaming data ingestion pipelines using AWS Glue, Amazon 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 Step Functions, including AWS Glue Data Catalog for metadata management and discovery.
• Configure Amazon Athena for serverless SQL querying across the data lake; support QuickSight integration with curated data sets for business analytics.
• Develop and deploy ML models on Amazon SageMaker for lead scoring, predictive maintenance, intelligent underwriting risk scoring, and AI-powered audience segmentation.
• Integrate Amazon Bedrock foundation models to enable generative AI 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 generation tasks.
• Design and implement entity resolution pipelines using Amazon Entity Resolution to identify, deduplicate, and merge customer records into unified golden records.
• Implement real-time and batch data synchronization pipelines between source systems and the Customer Data Platform (CDP).
• Support Azure data lake migration: conduct discovery, assess schemas and transformation logic, provision AWS target environments, execute migration via AWS DataSync, and perform data validation and reconciliation.
• Implement data lake security using AWS Lake Formation, including row-level security and column-level encryption.
• Build and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards.
• Ensure data quality, validation, and integrity across all 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 cloud environments.
• Strong proficiency in Python and SQL; experience with AWS data services including S3, Glue, Athena, Kinesis, and Step Functions.
• Hands-on experience with Amazon SageMaker for model development, training, tuning, and endpoint deployment.
• Working knowledge of Amazon Bedrock for integrating and applying foundation models in production-grade pipelines.
• Experience designing and implementing multi-zone data lake architectures on Amazon 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 management concepts and tools.
• Solid understanding of data modeling, feature engineering, data quality practices, and ML integration testing.
• Experience with AWS Lambda and AWS Step Functions for serverless workflow orchestration.
• Familiarity with Amazon 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