Data Architect

Position: Data Architect

Location: New York/New Jersey / Minnesota

Job Description

Job Title:

Role Summary


We are seeking a highly skilled and forward-thinking Data Architect who combines strong strategic vision with hands-on technical expertise . This role will be responsible for defining the organization's data strategy, designing scalable data architectures, and actively contributing to implementation across all data-driven initiatives.


The ideal candidate will bridge business and technology, ensuring data solutions align with organizational goals while being robust, secure, and future-ready.


Key Responsibilities

1. Data Strategy & Governance

• Define and drive the enterprise data strategy , aligned with business objectives.
• Establish data governance frameworks , policies, standards, and best practices.
• Lead data architecture roadmaps covering data platforms, integration, storage, and analytics.
• Partner with business stakeholders to identify data-driven opportunities and ensure value realization.
• Ensure compliance with data privacy, security, and regulatory requirements .

2. Architecture Design

• Design and own end-to-end data architecture (batch, real-time, streaming).
• Develop scalable solutions across:
  • Data Warehousing / Data Lakes / Lakehouses
  • Cloud platforms (Azure, AWS, GCP)
  • Big Data ecosystems
• Define data models (conceptual, logical, physical) for enterprise systems.
• Establish standards for data integration, APIs, and metadata management .

3. Hands-on Technical Leadership

• Actively participate in solution design and development (not just oversight).
• Build and review:
  • Data pipelines (ETL/ELT)
  • Data ingestion frameworks
  • Data transformation processes
• Optimize performance, scalability, and reliability of data systems.
• Conduct architecture reviews, code reviews, and design validations .

4. Platform & Technology Enablement

• Evaluate and recommend tools, technologies, and platforms .
• Drive adoption of modern practices such as:
  • DataOps
  • MLOps (where applicable)
  • Data Mesh / Data Fabric paradigms
• Enable self-service analytics and business intelligence capabilities.

5. Stakeholder Management

• Collaborate with:
  • Business leaders
  • Engineering teams
  • Product managers
• Translate business requirements into technical solutions.
• Mentor and guide data engineers, analysts, and other architects.