3 days ago
Responsibilities
- Architect, build, and maintain Snowflake-powered cloud data pipelines and modern datalake environments.
- Design and manage automated ETL/ELT ingestion and transformation pipelines for structured, semi-structured, and unstructured financial data.
- Lead legacy data-store migrations, including data mapping, transformation logic, and architectural consistency.
- Implement reconciliation, schema validation, de-duplication, and financial data-integrity controls for analytics and regulatory reporting.
- Drive product roadmaps, technical backlog prioritization, feature delivery, technical-debt management, and platform reliability.
- Lead initiatives from business requirements and technical design through development, integration testing, UAT, cutover, and go-live.
- Translate strategic objectives into epics, technical user stories, and acceptance criteria in Jira.
- Coordinate business users, quantitative and analytics teams, and engineering leads while managing risks, timelines, and operational readiness.
Requirements
- 8+ years of hands-on data engineering, technical product management, or technical architecture experience.
- Direct financial-services experience handling sensitive data in alternative investments such as private equity, hedge funds, private credit, or real estate.
- 8+ years of production Python development for large-scale data processing, performance optimization, and automation using Pandas, Polars, PySpark, or equivalent.
- 8+ years of advanced SQL experience including window functions, common table expressions, recursive queries, dynamic conditional logic, and query optimization.
- Production experience deploying and optimizing Snowflake solutions with AWS, Azure, or GCP.
- Experience managing end-to-end project lifecycles, release management, operational cutover plans, and Jira/Confluence delivery frameworks.
- Experience designing automated continuous reconciliation frameworks for accounting, portfolio, or transactional data.
- Familiarity with Airflow, Prefect, or Dagster and data-transformation frameworks such as dbt.
- Experience with regulatory and governance standards such as SOC2, GDPR, or BCBS 239 in buy-side or sell-side financial institutions.
- Bachelor's or master's degree in Computer Science, Data Engineering, Information Systems, Finance, or a related quantitative discipline.
Benefits
- Full-time role based in New York, USA.
- Opportunity to work with Fortune 500 companies and innovative market disruptors.
- Exposure to artificial intelligence, machine learning, data, and cloud technologies.
- Opportunity to join a growing AI-first digital transformation and engineering company.
Tech Stack
Categories
Data Engineering
About Quantiphi
Quantiphi is an AI-first digital engineering and consulting firm that designs and implements machine learning, data, and cloud solutions for large enterprises across industries. Its teams build production systems—such as generative AI applications, computer vision, and predictive analytics—primarily on Google Cloud and other hyperscalers, delivered as professional services and managed solutions. Founded in 2013 and headquartered in Marlborough, Massachusetts, the company is privately held and a Google Cloud partner.