Senior Data/ML Engineer – AWS

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Capstone Integrated Solutions

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51 - 200 employees

Capstone Integrated Solutions is a comprehensive services provider. Our team consists of outstanding professionals, highly experienced in designing, building, and supporting retail software. We see ourselves as a build-as-a-service provider who follows a repeatable business pattern that can be applied to a variety of platforms and verticals. Having a culture built on outcomes and delivery at the core of the business, Capstone is providing its customers with a complete suite of services for software development, system analysis, integration, implementation, and support, as well as the option to engage a single team to perform all the services they require.

📋 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