Senior Data Engineer

Gen II is building out its Data & Integration Platform — 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.

We are looking for a Senior Data Engineer to join the DIP team and take direct ownership of data layer delivery — building and maintaining the Bronze, Silver and Gold layers that turn raw system data into trusted, analytics-ready assets. Like our integration practice, our data engineering function is AI-assisted: we expect you to use AI tooling as a natural part of how you work, whether that’s generating transformation logic, scaffolding Streamlit applications, or accelerating documentation.

You will be expected to come in, get up to speed quickly, and drive data workstreams forward with minimal oversight. You will report to and work closely with the Associate Director of Data Engineering to establish and maintain the data platform standards and patterns.

Building and maintaining Bronze à Gold layers within Snowflake — effectively moving data throughout 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 — 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 — generating transformation SQL, scaffolding Python scripts, producing test cases, and automating documentation; AI assistance is the default mode of working, not a selective shortcut

Contributing to a data factory approach — reusable, metadata-driven patterns for transformation, testing, and deployment that can be applied consistently across data streams

Building Streamlit applications in Snowflake for internal operational tooling — data review queues, reconciliation dashboards, client provisioning workflows, and similar

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 — lineage, cataloguing, access control, and data quality standards

5+ years of hands-on data engineering experience — evidenced in role history, not just a skills list

Deep practical Snowflake expertise — data modelling, SQL optimisation, Streams/Tasks/Stored Procedures, Secure Data Shares; SnowPro certification a plus

Strong Python development — 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; this should be embedded in how you work, not aspirational

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 with the ability to collaborate across technical and business teams