Senior Data Engineer

Gen II is a leading fund administration provider focused on private capital asset managers and investors, managing more than $1.5 trillion in private fund capital assets. Job Summary We are building out our Data & Integration Platform (DIP)—the central data backbone that powers data integration, analytics, reporting, and AI capabilities across our fund administration technology estate. At the heart of DIP is a Snowflake‑based medallion data architecture—Bronze, Silver, and Gold—fed by integrations across 40+ systems and consumed by business intelligence, client‑facing products, and AI‑driven services. This role is for a Senior Data Engineer who will own the data layer delivery, build and maintain the Bronze, Silver, and Gold layers, and drive data workstreams with minimal oversight. Snowflake Data Layer Build Build and maintain Bronze to Gold layers within Snowflake—moving data for reporting, analytics, and egress. Design and implement Snowflake data models aligned to Gen II’s canonical domain model—fund structures, investors, GL, NAV, compliance. Develop and maintain Snowflake Streams, Tasks, and Stored Procedures for pipeline orchestration and incremental processing. Own data quality—implement validation, monitoring, and alerting within the pipeline to ensure Gold‑layer data is trustworthy. Work with Fivetran‑synced data from source systems and Snowflake Secure Data Shares, building clean ingestion patterns at the Bronze/Silver boundary. AI‑Assisted Development Use AI tooling (LLMs, codegen, Claude) across all data engineering work—generating transformation SQL, scaffolding Python scripts, producing test cases, automating documentation. Contribute to a data factory approach—reusable, metadata‑driven patterns for transformation, testing, and deployment that can be applied consistently across data streams. Python & Streamlit Development Build Streamlit applications in Snowflake for internal operational tooling—data review queues, reconciliation dashboards, client provisioning workflows, and similar. Write Python scripts and utilities to support data processing, validation, and operational tasks within the DIP ecosystem. Contribute to pytest‑based test suites for data pipeline validation, aligned to DIP QA standards. Analytics & BI Enablement Structure Gold‑layer outputs to serve downstream consumers—Power BI, Qlik, and client‑facing analytics products. Collaborate with BI specialists to ensure data models support reporting requirements without duplication or technical debt. Support the establishment of master data management and golden record patterns across key fund administration entities. Delivery & Collaboration Take data workstreams from requirement to production with minimal hand‑holding. Work closely with the Associate Director of Data Engineering, integration engineers, BAs, and QA—raising blockers early and documenting decisions clearly. Collaborate with integration engineers to ensure data lands cleanly at the Bronze/Silver boundary and conforms to agreed schemas. Contribute to data governance practices—lineage, cataloguing, access control, and data quality standards. Qualifications 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—data modelling, SQL optimisation, Streams/Tasks/Stored Procedures, Secure Data Shares; SnowPro certification a plus. Strong Python development skills—data processing scripts, utilities, and Streamlit applications. Experience building Bronze/Silver/Gold medallion architectures or equivalent layered data warehouse patterns. Hands‑on experience with Fivetran or equivalent ELT tooling for source system ingestion desired. 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. Experience with data governance, data quality frameworks, and master data management (desirable). Exposure to fund administration, private capital, or broader financial services environments (desirable). Familiarity with AI/ML concepts and their application to financial services data (desirable). Strong communication skills and ability to collaborate across technical and business teams. Compensation The salary range for this position is $140,000 – $170,000, in addition to a discretionary bonus and a comprehensive benefits package. The actual salary offered within that range will depend on the candidate’s experience level. Work Arrangement This role follows a flexible hybrid schedule, with the resource expected in the office 1‑4 days per week on a need basis. All applicants must be legally authorized to work in the United States. #J-18808-Ljbffr