AWS Data Engineer

Saxon Global•New York City, NY

5d•Remote

About The Position

We are seeking an AWS Data Engineer to join our Data team. This role focuses on building production-grade data pipelines and integrations on AWS, utilizing serverless technologies and infrastructure as code. You will translate business requirements into practical data solutions, enabling self-service analytics and real-time data access across various enterprise systems. The ideal candidate will have extensive experience with AWS data services, Snowflake, SQL, and Python, with a strong understanding of CI/CD practices and cloud cost optimization.

Requirements

5 to 7 years designing and supporting data integrations and pipeline orchestration on AWS (Lambda, Step Functions, Fargate, Glue, etc).
Snowflake experience required, 5 plus years.
3 plus years with SQL and Python.
Strong written and verbal communication skills.
Master's in computer science, or bachelor's with equivalent experience.

Nice To Haves

Retail industry exposure a plus.
Experience with an integration platform like Oracle Integration Cloud a plus.
Reporting tool experience such as Sigma a strong plus.
Salesforce ecosystem exposure preferred (SFCC, SFSC, SFMC).
Snowflake or Salesforce certifications a plus.

Responsibilities

Build production-grade pipelines on AWS serverless tools with solid error handling, retry logic, and observability.
Implement data warehousing for historical tracking and compliance.
Build incremental processing with change data capture and idempotent design.
Create multi-stage orchestration with parallel execution.
Write and run test scripts before production release.
Connect enterprise systems (ERP, CRM, ecommerce, third party APIs) to each other and to the cloud data warehouse.
Build event-driven flows using AWS messaging and streaming tools.
Design RESTful APIs with proper authentication for real-time access.
Build integrations spanning retail, supply chain, and finance systems.
Manage cloud resources as code using Terraform.
Build multi-environment deployment pipelines with automated testing.
Set up monitoring and alerting for pipeline health, data quality, and SLA tracking.
Optimize cloud costs through serverless design and right sizing.
Define security guardrails for data integrations.