Snowflake Data Engineer

Job Summary

Location:

Jersey City, NJ – 4 Days onsite role. Long Term Project. We are seeking an experienced Senior Data Engineer with strong financial domain experience to design, build, and manage scalable data platforms. The ideal candidate will have deep hands-on expertise with Snowflake, dbt, Python, Airflow, Kubernetes
, and SQL
, along with a solid understanding of data architecture and design principles
. This role requires someone who can work independently, take ownership of deliverables, and collaborate effectively with both technical and business stakeholders.

• 7–8+ years of hands-on experience in Data Engineering or related roles.
• Strong experience working in the financial services domain (banking, capital markets, risk, finance, or similar).
• Extensive hands-on experience with Snowflake in production environments.
• Strong expertise in SQL (complex queries, performance optimization, analytical patterns).
• Experience building and managing data transformations using dbt
  .
• Proficient in Python for data processing and orchestration.
• Hands-on experience with Apache Airflow for workflow orchestration.
• Working knowledge of Kubernetes and containerized deployments.
• Solid understanding of data modeling, data warehousing, and data architecture concepts
  .
• Experience working independently and owning end-to-end delivery.
• Strong problem-solving and analytical skills.

• Design, develop, and maintain robust, scalable, and high-performance data pipelines and data platforms.
• Architect and implement cloud-based data solutions with a strong focus on Snowflake.
• Build and manage transformation frameworks using dbt and SQL best practices.
• Develop and orchestrate batch and near real-time data workflows using Airflow.
• Containerize and deploy data workloads using Kubernetes where applicable.
• Support data modeling and design activities, including work on analytical data structures such as data marts.
• Contribute to modernization efforts, including migration of legacy on-premises databases (e.g., Oracle) to cloud data platforms.
• Ensure data quality, reliability, performance, and security across data pipelines.
• Collaborate with cross-functional teams including Data Architects, Analytics, Product, and Business stakeholders.
• Identify opportunities to improve development efficiency by leveraging modern tooling, automation, and AI-based agents and tools.
• Act as a senior individual contributor, independently managing tasks, priorities, and deliverables.

Adhere to software engineering best practices, including version control, code reviews, documentation, and CI/CD.