Data Architect (Manager Level) - New York, NY (Onsite)

Job Title: Data Architect

Location: New York, NY (Onsite)

Experience Required: 14+ Years

About the Role

We are seeking an experienced Data Architect (Manager Level) with deep hands-on expertise in modern data engineering, large-scale distributed systems, and cloud-native analytics platforms. The ideal candidate brings strong technical leadership, architecture design capabilities, and end-to-end data ecosystem knowledge across cloud and on-prem environments.

Key Responsibilities

• Architect and deliver large-scale, high-volume data engineering and analytics solutions across cloud and hybrid infrastructures.
• Lead end-to-end design of data ingestion, integration, transformation, and data modeling frameworks.
• Build and optimize ETL/ELT pipelines, analytical environments, and data lakes.
• Drive architecture standards for PySpark, Kafka streaming, and Snowflake ecosystems.
• Establish enterprise frameworks covering data governance, metadata management, lineage, MDM, and reference data.
• Collaborate with Agile teams and senior stakeholders to align solutions with business objectives.
• Evaluate and implement modern data engineering tools, techniques, and best practices.
• Act as the primary technical lead for critical programs and integrations, ensuring timely issue resolution.

Must-Have Skills

• 14+ years in IT with 12+ years in hands-on data engineering.
• Expert-level:
  • PySpark (large-scale processing)
  • Kafka (real-time streaming)
  • Snowflake (cloud data warehouse)
• 10+ years in defining enterprise data & analytics architecture.
• Proven experience delivering large, complex data projects across cloud (preferably AWS) and on-prem setups.
• Strong experience with Python, Kafka APIs, Talend, SAP BODS, and big data tools.
• Deep expertise in SQL and NoSQL databases.
• Strong experience with data modeling using tools such as:
  • erwin Data Modeler
  • ER/Studio
• Solid exposure to BI/visualization tools:
  • QlikView
  • Qlik Sense
  • Tableau
• Strong grounding in data warehousing, data marts, metadata, dependencies, and workload management.
• Experience working in Agile environments.

Nice-to-Have

• Experience setting up enterprise-scale data lakes.
• Familiarity with MDM/RDM platforms.
• Strong communication and stakeholder management skills.