Senior Data Engineer in New York at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in United States.

This is a senior-level opportunity to lead the design, development, and scaling of a data warehouse and analytics platform supporting enterprise customers and internal teams. You will shape the architecture behind secure data isolation, warehouse pipelines, and provisioning infrastructure. A key focus will be enforcing database-level access controls while enabling customers to run ad-hoc queries against shared data infrastructure. Internally, you will build reliable models, dashboards, and reporting systems that teams can use with confidence. You’ll collaborate closely with engineering, support, customer success, and other stakeholders to improve automation, scalability, data quality, and compliance. The role is ideal for a hands-on engineer who embraces AI-assisted development and builds reusable systems that can grow with the business.

• Lead the design, development, and scaling of the data warehouse and analytics platform.
• Design and maintain data isolation, warehouse pipelines, and provisioning infrastructure that securely serves enterprise customers.
• Implement and strengthen database-level access controls, including row-level and column-level security, RBAC, and role hierarchies.
• Ensure customers can access their own data through ad-hoc queries while maintaining strict data isolation and security guarantees.
• Operate and troubleshoot production data pipelines, including CDC failures, schema drift, and problematic upstream data.
• Build reusable, automated provisioning and data infrastructure using Terraform or similar infrastructure-as-code tools.
• Partner with engineering, support, customer success, and other teams to translate reporting needs into reliable data models and dashboards.
• Improve automation, observability, data quality, scalability, and compliance across the data environment.
• Monitor cloud infrastructure costs and identify the configurations and engineering decisions driving spend.
• Leverage AI-powered development tools and help promote modern, AI-assisted engineering practices across the organization.

Requirements:

• 6+ years of engineering experience spanning data engineering and infrastructure.
• Strong proficiency in SQL and relational databases, with PostgreSQL and Snowflake experience preferred.
• Proven experience implementing database-level access controls in a data warehouse, including row-level or column-level security, RBAC, and role hierarchies.
• Strong ability to reason about data access paths, including unintended query paths and potential security boundaries.
• Experience operating production data pipelines and diagnosing issues such as CDC failures, schema drift, and inaccurate upstream values.
• Fluency with Terraform or similar infrastructure-as-code tools and a preference for automated, repeatable provisioning.
• Experience working with non-engineering stakeholders to translate ambiguous reporting requirements into practical, reusable data models.
• Comfortable using scripting languages for automation; experience with Ruby and SQL is relevant.
• Ability to build scalable, reusable tools rather than relying on one-off scripts.
• Strong focus on data quality, observability, security, and compliance.
• Clear communication skills and the ability to collaborate effectively with both technical and non-technical teams.
• Experience working with HIPAA-regulated data is strongly preferred, particularly where PHI isolation is a core requirement.
• Experience with dbt, Metabase, Kafka/Debezium, or AWS data infrastructure is a plus.
• A curious, adaptable, hands-on mindset with a strong interest in automation and AI-assisted engineering practices.

Benefits:

• Full-time, fully remote position.
• Base salary of $185,000–$215,000.
• U.S. work authorization required.
• Opportunity to work on business-critical data infrastructure supporting enterprise customers and healthcare organizations.
• Exposure to large-scale data systems, secure data isolation, automation, and AI-enabled engineering practices.
• Collaborative environment involving engineering, customer success, support, and other cross-functional teams.
• Opportunity to build scalable systems with a strong focus on data quality, security, and compliance.