Senior Data Architect

New York Technology Partners; Chicago (IL); On-site; USD 120,000 - 150,000; Full time

Job summary

New York Technology Partners is seeking an experienced Data Architect in Chicago. This role focuses on building the foundation for our analytics ecosystem, where you'll lead the design of our data warehouse and ensure data governance.

Ideal candidates will possess extensive experience in data warehouse design and ETL pipeline implementation. Proficiency in AWS and strong SQL skills are essential. A collaborative mindset and ability to work on-site are required.

Qualifications

• Extensive hands-on experience designing and implementing data warehouses.
• Proven experience building robust ETL/ELT pipelines for high-volume data.
• Strong understanding of data privacy and secure data handling.

Responsibilities

• Build and maintain a scalable data warehouse optimized for performance.
• Design and implement efficient ETL/ELT pipelines for structured data.
• Establish best practices for data governance and compliance.

Skills

Data modeling

ETL/ELT pipeline design

AWS (Redshift, S3, Glue, Athena)

Python for data processing

Data governance and compliance

SQL expertise

Education

Bachelor’s degree in Computer Science or related field

Tools

Looker

Tableau

Power BI

Job description

We’re looking for an architect‑level data leader — someone who can design end‑to‑end data solutions, define structure and governance, and build the systems that make data usable, trusted, and scalable across the company. This is not a junior role. We need someone who understands how to take large, complex data sets and architect the right models, pipelines, and warehouse structures to unlock real value.

About the Role

We’re seeking a Data Architect to build the backbone of our analytics ecosystem. This role goes far beyond pipelines and performance. It’s about creating a foundation that enables teams to ask better questions, move faster, and make smarter decisions. You’ll lead the buildout of our data warehouse and ingestion pipelines, partnering closely with engineering and product teams to ensure seamless intake and organization of metrics across all products and services. You’ll also help define how the company thinks about data — establishing best practices, shaping governance, and laying the groundwork for reporting, analytics, and future AI initiatives.

Key Responsibilities

• Build and maintain a scalable, secure data warehouse optimized for performance and long‑term growth
• Design and implement efficient ETL/ELT pipelines for structured and semi‑structured data
• Partner with engineering and product teams to intake, structure, and leverage new data sources
• Develop data models and schemas that support analytics, reporting, and AI/ML use cases
• Establish best practices for data governance, privacy, security, and compliance
• Implement monitoring and alerting to ensure reliability and accuracy of data ingestion
• Document architecture, decisions, and workflows to support transparency and knowledge‑sharing

Required Qualifications

• Extensive hands‑on experience designing and implementing data warehouses from the ground up
• Expertise with AWS (Redshift, S3, Glue, Athena) or Google BigQuery; experience with both is a plus
• Advanced SQL and strong data‑modeling skills
• Proven experience building robust ETL/ELT pipelines for high‑volume or real‑time data
• Strong understanding of data privacy, compliance (GDPR/CCPA), and secure data handling
• Proficiency with Python for data processing and automation
• Excellent communication skills and a collaborative mindset
• Must be willing and able to work on‑site in Chicago

Preferred Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or related field (or equivalent experience)
• Experience in a startup or fast‑paced environment
• Familiarity with visualization tools such as Looker, Tableau, or Power BI
• Knowledge of event‑driven architectures and streaming technologies (Kafka, Kinesis, Pub/Sub)
• Interest in mentoring and helping build a strong internal data community