Senior Data/ML Engineer (AWS)

CapNexus•New York, NY

16h•Remote

About The Position

Capnexus is looking for a highly skilled Senior AWS Data/ML Engineer to lead data architecture, pipeline development, and data integrations. This is an exciting opportunity to apply advanced cloud data engineering skills on a platform that leverages generative AI to automate and modernize enterprise workflows.

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.

Nice To Haves

Experience with Azure Data Lake, Azure Data Factory, or Azure Synapse — particularly in cloud-to-cloud migration contexts.
Familiarity with Amazon Entity Resolution for customer identity and deduplication use cases.
Experience with MLOps practices including model monitoring, drift detection, and automated retraining on SageMaker.
Experience with LLM prompt engineering, RAG architectures, or fine-tuning workflows on Amazon Bedrock.
Knowledge of Amazon QuickSight for analytics dataset preparation and embedded dashboard development.
AWS Certification (Machine Learning Specialty, Data Analytics Specialty, or Solutions Architect).
Background in real estate, property management, marketing technology, or insurance industries.

Responsibilities

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.