Data Engineer - Snowflake / AWS / ML Ops

Job Description

Job Description
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Requirements
Position Title: Data Engineer
Location: Onsite – 5 days/week in New York, NY or Boston, MA
Duration: 6–12 Months Contract
Experience Level: 5+ Years
About the Role:
We are seeking a highly skilled Senior Data Engineer to design, build, and optimize scalable data architectures that power analytics, machine learning, and enterprise applications. You will work closely with cross-functional teams, applying your expertise in SQL, Snowflake, Python, and cloud-native tools to deliver high-quality, secure, and efficient data solutions in a fast-paced financial services environment.

Key Expertise Required (Top Priorities):

• Deep knowledge of data modeling and data architecture for enterprise-scale applications.
  

• Strong understanding of ML Ops and how it leverages data to deliver predictive and AI-driven outcomes.
  

• Proven experience implementing data architectures that support data science models, including prediction, machine learning, and advanced analytics.
  
  

Core Responsibilities:

• Data Architecture & Modeling: Design, implement, and maintain enterprise-grade data architectures, ensuring optimal performance, scalability, and reliability.
  

• ELT Development: Build robust ELT frameworks using DBT, MWAA, and Snowflake from the ground up.
  

• SQL & Snowflake Expertise: Write optimized SQL queries, perform performance tuning, and implement best practices in data modeling.
  

• Python Development: Develop automation scripts, data integration pipelines, and REST API connectors using Python.
  

• Workflow Orchestration: Leverage Apache Airflow for scheduling, orchestration, and monitoring of data workflows.
  

• Cloud Integration: Work with AWS services (S3, Lambda, IAM, CloudWatch) to manage data pipelines, security, and monitoring.
  

• Data Quality & Governance: Implement processes to ensure accuracy, consistency, and compliance with enterprise data governance and security policies.
  

• ML Ops Integration: Architect data flows that enable machine learning models, prediction systems, and RAG (Retrieval-Augmented Generation) workflows.
  

• Collaboration & Agile Practices: Partner with UX teams, participate in design discussions, code reviews, and Agile ceremonies to continuously improve deliverables.
  

• Security & Compliance: Follow best practices for secure coding, access control, and compliance—especially for financial data systems.
  
  

Required Skills & Experience:

• 5+ years of professional experience as a Data Engineer.
  

• Strong SQL and Snowflake skills, including performance tuning and advanced data modeling.
  

• Proven track record in building (not just consuming) ELT frameworks using DBT and MWAA.
  

• Proficiency in Python for automation, scripting, and REST API integrations.
  

• Hands-on experience with Apache Airflow for orchestration and workflow monitoring.
  

• Expertise with AWS services including S3, Lambda, IAM, CloudWatch.
  

• Experience in integrating and processing data from REST APIs.
  

• Understanding of data quality, governance, and cloud-native troubleshooting.
  

• Exposure to RAG workflows and data-driven ML model enablement.
  

• Familiarity with Agile or Scrum methodologies.
  
  

Preferred Qualifications:

• Experience in financial services or other highly regulated industries.
  

• Strong understanding of secure coding practices and compliance frameworks.
  

• Hands-on ML Ops pipeline development and deployment experience.
  

Benefits
Visit us at http://alignity.io/careers. Alignity Solutions is an Equal Opportunity Employer, M/F/V/D.

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