Senior Data Engineer - Data Proposition Products

What's the role?

Gen II is launching multiple data proposition products: extracting clean, validated and normalized data from our fund administration platforms & financial statements and making it available to clients and internal teams as a trusted asset they can depend on and build on. This is a scalable data offering designed to differentiate Gen II in the market-delivering these datasets through our Sensr product, a commercialized analytics portal that differentiates our offering and eliminates client integration overhead. More strategically, you'll lead the architecture of new data products built on this foundation, collaborating with product and go-to-market teams to commercialize platform data into data offerings that drive revenue and competitive advantage.

We are looking for a Senior Data Engineer to lead the extract  validate  normalize  store pipeline. Your mission is to own the journey from raw source data to client-ready assets. You'll design validation and QC rules in dbt, build normalized schemas in Snowflake, and ensure every dataset that leaves our platform is trustworthy, well-documented, and easy for clients to consume. 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 and Head of Data Product.

What you'll be doing

Validation & QC Rules - Ensuring Data Clients Can Trust

• Building and maintaining dbt models that implement comprehensive validation and QC rules on source data.
• Designing rule sets for key fund administration entities-funds, investors, GL accounts, NAV components-ensuring data integrity at ingestion.
• Monitoring and alerting data quality issues before normalized assets reach clients.
• Documenting validation rules and exceptions so clients understand what they can rely on.

Data Normalization in Snowflake - From Raw to Client-Ready

• Building and maintaining the Medallion architecture (Bronze ingestion, Silver transformation via dbt with validation/QC rules, Gold normalization) to create curated datasets aligned to Gen II's core data model (fund structures, investors, GL, NAV, compliance).
• Enabling multi-channel data delivery: making Gold layer datasets available through Sensr Portal's Analytics & Databridge for client consumption, while simultaneously powering internal analytics, reporting, and AI-driven services.
• Building Snowflake Streams and Tasks for incremental processing-keeping normalized datasets fresh without full reprocessing.

Client Data Enablement - Making Data Self-Serve

• Building Streamlit applications for client-facing data access-dashboards, export tools, data validation status, usage metrics.
• Creating and maintaining data dictionaries and lineage documentation that clients need to onboard and trust normalized data.
• Collecting client feedback on data quality, schema design, and access patterns to drive continuous improvement.

AI-Assisted Development & Factory Patterns

• Using AI tooling (LLMs, Claude) across all work-generating dbt rules, transformation SQL, Streamlit scaffolding, test cases, and documentation.
• Building reusable, metadata-driven patterns for validation, transformation, and deployment so the team can scale the pipeline without reinventing each step.

Delivery & Cross-Functional Collaboration

• Taking data workstreams from requirement to production with minimal hand-holding.
• Working closely with product, integration, and client success teams to understand what data clients need and how to deliver it.
• Collaborating with the Head of Data Product to ensure data flows cleanly from source through normalization to client delivery.
• Contributing to data governance practices-lineage, cataloguing, access control, and quality standards that support both internal ops and external consumption.

The ideal background for this role:

• 5+ years of hands-on data engineering experience - evidenced in role history, not just a skills list.
• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field
• Deep practical dbt expertise-writing and maintaining transformation logic at scale, testing, and documentation.
• Strong Snowflake knowledge-data modelling, SQL optimization, Streams/Tasks for incremental processing, Secure Data Shares.
• Strong Python development-data processing scripts, utilities, and Streamlit applications.
• Experience building and owning data pipelines from ingestion to consumption.
• Hands-on experience with Fivetran or equivalent ELT tooling for source system integration.
• Active use of AI-assisted development in data engineering delivery; this should be embedded in how you work, not aspirational.
• Experience thinking about data as a product; designing schemas, documentation, and access patterns that external or cross-team consumers depend on.
• Ability to own workstreams independently and drive delivery without close management.
• Experience with data quality frameworks and validation rule design (desirable).
• Exposure to fund administration, private capital, or financial services data environments (desirable).
• Familiarity with client onboarding processes or APIs (desirable).
• Strong communication skills with the ability to translate technical decisions into client-friendly language.

The salary range for this position is $140,000 - $170,000, in addition to a discretionary bonus and comprehensive benefits package. Please note that the actual salary offered within that range will depend on the candidate's experience level.

Work Arrangement

All applicants applying to Gen II Fund Services, LLC must be legally authorized to work in the United States. This role follows a flexible hybrid schedule, with the resource expected in the office 1-4 days per week on a need basis.

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