Senior Data/ML Engineer – AWS

Capstone Integrated Solutions

Senior AWS Data/ML Engineer at Capnexus leading cloud data engineering and integrations to modernize workflows using AI technologies.

Posted 6/8/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

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Hard Skills

PythonSQLAWS GlueAmazon S3Amazon KinesisAWS Step FunctionsAmazon SageMakerAmazon BedrockAWS Lambdadata modeling

Soft Skills

analytical skillsproblem-solving skillscommunication skillscollaborationAgileScrum

Tools & Technologies

AWS Data PipelineAmazon AthenaAWS Lake FormationKiro CLIAmazon API GatewayAWS DataSyncQuickSightentity resolution toolsdata governance documentationCustomer Data Platform (CDP)

Industry Keywords

data engineeringML engineeringdata lake architecturedata qualityfeature engineeringmaster data managementdata validationUATdata discoverydata synchronization

Tech Stack

Tools & technologies

AWSAzureCloudETLPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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