Senior Data/ML Engineer – AWS

This is a fully remote position, open to applicants in United States.

📋 Description

• Engage in data discovery workshops to catalog source systems, which include property management platforms, marketing channels, and CRM data, translating the insights into data lake architectural requirements.

• Architect and execute a multi-zone enterprise data lake on Amazon S3 (raw, conformed, enriched, aggregated), ensuring the design aligns with the Statement of Work (SOW) architecture through ingest, cleansing, and business layers.

• Develop batch and streaming data ingestion pipelines utilizing AWS Glue, Amazon Kinesis, and AWS Data Pipeline across Customer Data Platforms (CDP), marketing, and property management data sources.

• Establish data transformation and orchestration frameworks using AWS Glue ETL and AWS Step Functions, incorporating the AWS Glue Data Catalog for effective metadata management and discovery.

• Set up Amazon Athena for serverless SQL querying within the data lake and facilitate QuickSight integration with curated datasets for business analytics purposes.

• Create and deploy Machine Learning (ML) models on Amazon SageMaker for applications such as lead scoring, predictive maintenance, intelligent underwriting risk evaluation, and AI-driven audience segmentation.

• Incorporate Amazon Bedrock foundation models to facilitate generative AI capabilities like customer profile enrichment, hyper-personalization, and intelligent marketing automation.

• Utilize Kiro CLI to enhance AI-assisted development workflows, specification-driven pipeline implementation, and automated code generation activities.

• Design and implement entity resolution pipelines using Amazon Entity Resolution to identify, deduplicate, and consolidate customer records into unified golden records.

• Establish real-time and batch data synchronization pipelines between source systems and the Customer Data Platform (CDP).

• Assist in Azure data lake migration by conducting discovery, evaluating schemas and transformation logic, provisioning AWS target environments, executing migration via AWS DataSync, and performing data validation and reconciliation.

• Enforce data lake security through AWS Lake Formation, including row-level security and column-level encryption.

• Construct and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards.

• Ensure data quality, validation, and integrity throughout all pipeline stages and ML model outputs, as well as support User Acceptance Testing (UAT) for data-dependent features.

• Collaborate with Full Stack, DevOps/MLOps, and AWS engagement teams while contributing to architecture documentation, pipeline runbooks, and data governance documentation.

⛳️ Requirements

• Over 5 years of experience in data engineering or ML engineering, including at least 2 years in AWS cloud environments.

• Strong expertise in Python and SQL, with experience in AWS data services such as S3, Glue, Athena, Kinesis, and Step Functions.

• Practical experience with Amazon SageMaker for the development, training, tuning, and deployment of models.

• Familiarity with Amazon Bedrock for the integration and application of foundation models within production-grade pipelines.

• Proven experience in designing and implementing multi-zone data lake architectures on Amazon S3, including lifecycle policies and governance via Lake Formation.

• Knowledge of Kiro CLI or similar AI-assisted/agentic development tools.

• Experience with entity resolution, deduplication, or master data management concepts and tools.

• Strong grasp of data modeling, feature engineering, data quality practices, and ML integration testing.

• Experience with AWS Lambda and AWS Step Functions for orchestrating serverless workflows.

• Familiarity with Amazon API Gateway for exposing data services and model endpoints.

• Excellent analytical, problem-solving, and communication skills; adept at collaborating within Agile/Scrum teams alongside AWS Professional Services.

🏝️ Benefits

• Opportunity for remote work.