Senior Data Engineer

Gen II Fund Services•New York, NY

13d•$140,000 - $170,000•Hybrid

About The Position

Gen II is building out its Data & Integration Platform (DIP) — the central data backbone that powers data integration, analytics, reporting, and AI capabilities across its fund administration technology estate. The DIP is built on a Snowflake-based medallion data architecture (Bronze, Silver, and Gold) fed by integrations across over 40 systems and consumed by business intelligence, client-facing products, and AI-driven services. This role involves taking direct ownership of data layer delivery, building and maintaining the Bronze, Silver, and Gold layers to transform raw system data into trusted, analytics-ready assets. The data engineering function is AI-assisted, expecting the use of AI tooling for tasks like generating transformation logic, scaffolding applications, and accelerating documentation. The Senior Data Engineer will report to and work closely with the Associate Director of Data Engineering to establish and maintain data platform standards and patterns, driving data workstreams forward with minimal oversight.

Requirements

5+ years of hands-on data engineering experience.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Deep practical Snowflake expertise, including data modeling, SQL optimization, Streams/Tasks/Stored Procedures, and Secure Data Shares.
Strong Python development skills for data processing scripts, utilities, and Streamlit applications.
Experience building Bronze/Silver/Gold medallion architectures or equivalent layered data warehouse patterns.
Active use of AI-assisted development in data engineering delivery (transformation generation, test scaffolding, documentation automation).
Experience with BI tooling (Power BI and/or Qlik) and structuring data models to serve reporting consumers effectively.
Ability to own workstreams independently and drive delivery without close management.
Strong communication skills with the ability to collaborate across technical and business teams.
Legally authorized to work in the United States.

Nice To Haves

SnowPro certification.
Hands-on experience with Fivetran or equivalent ELT tooling for source system ingestion.
Experience with data governance, data quality frameworks, and master data management.
Exposure to fund administration, private capital, or broader financial services environments.
Familiarity with AI/ML concepts and their application to financial services data.

Responsibilities

Building and maintaining Bronze to Gold layers within Snowflake for reporting, analytics, and egress to downstream systems.
Designing and implementing Snowflake data models aligned to the Gen II canonical domain model (fund structures, investors, GL, NAV, compliance).
Developing and maintaining Snowflake Streams, Tasks, and Stored Procedures for pipeline orchestration and incremental processing.
Owning data quality by implementing validation, monitoring, and alerting within the pipeline to ensure Gold layer data is trustworthy.
Working with Fivetran-synced data from source systems and Snowflake Secure Data Shares, building clean ingestion patterns at the Bronze/Silver boundary.
Using AI tooling (LLMs, codegen, Claude) across all data engineering work, including generating transformation SQL, scaffolding Python scripts, producing test cases, and automating documentation.
Contributing to a data factory approach with reusable, metadata-driven patterns for transformation, testing, and deployment.
Building Streamlit applications in Snowflake for internal operational tooling (data review queues, reconciliation dashboards, client provisioning workflows).
Writing Python scripts and utilities to support data processing, validation, and operational tasks within the DIP ecosystem.
Contributing to pytest-based test suites for data pipeline validation, aligned to DIP QA standards.
Structuring Gold layer outputs to serve downstream consumers (Power BI, Qlik, and client-facing analytics products).
Collaborating with BI specialists to ensure data models support reporting requirements without duplication or technical debt.
Supporting the establishment of master data management and golden record patterns across key fund administration entities.
Taking data workstreams from requirement to production with minimal hand-holding.
Working closely with the Associate Director of Data Engineering, integration engineers, BAs, and QA, raising blockers early and documenting decisions clearly.
Collaborating with integration engineers to ensure data lands cleanly at the Bronze/Silver boundary and conforms to agreed schemas.
Contributing to data governance practices, including lineage, cataloguing, access control, and data quality standards.