Senior Data Engineer - Financial Services

Role- Senior Data Engineer (Snowflake / Python / Financial Services) Location- Onsite – New York City, NY (No Remote Option) Experience Required – 10+Years Employment Type -Contract Industry Preference - Asset Management / Capital Markets / Financial Services Job Summary We are seeking two highly experienced Senior Data Engineers to design, build, and scale a modern investments data platform. The ideal candidates will have strong expertise in Snowflake, SQL, Python, and modern ELT frameworks, along with experience handling complex financial datasets. This is a hands-on role in a lean, high-impact engineering team where you will help shape the architecture, development standards, and long-term scalability of the investment data platform. Key Responsibilities • Design, develop, and maintain scalable data pipelines and data platforms. • Build and optimize data solutions using Snowflake. • Develop ELT workflows using modern frameworks such as dbt, Airflow, or similar tools. • Write efficient and complex SQL queries for large-scale financial datasets. • Develop Python-based data processing and automation scripts. • Design and implement robust data models to support investment and financial analytics. Required Skills and Qualifications • 10–15 years of experience in data engineering. • Strong hands-on experience with Snowflake. • Advanced SQL skills. • Proficiency in Python for data engineering and automation. • Experience with modern ELT frameworks (dbt, Airflow, Matillion, or similar). • Strong understanding of dimensional modeling and data warehousing concepts. • Experience designing data models for complex financial datasets. • Knowledge of data governance, lineage, and quality controls. • Experience building scalable and maintainable data architectures. Nice-to-Have Skills • Experience with cloud platforms (AWS, Azure, or GCP). • Knowledge of orchestration and workflow scheduling tools. • Familiarity with DevOps, CI/CD, and infrastructure-as-code. • Experience with market, portfolio, performance, and risk data. • Exposure to data cataloging and metadata management tools.