Senior Data Engineer – Data Proposition Products

Gen II Fund Services•New York, NY

2d•$140,000 - $170,000•Hybrid

About The Position

Gen II is launching multiple data proposition products, aiming to extract, validate, and normalize data from fund administration platforms and financial statements. This data will be made available to clients and internal teams as a trusted asset through their Sensr product, a commercialized analytics portal. The role involves leading the architecture of new data products, collaborating with product and go-to-market teams to commercialize platform data into revenue-driving offerings. The Senior Data Engineer will own the entire pipeline from raw source data to client-ready assets, designing validation and QC rules in dbt, building normalized schemas in Snowflake, and ensuring data trustworthiness, documentation, and ease of consumption. AI tooling is expected to be used as a natural part of the workflow for tasks like generating transformation logic and accelerating documentation. The role requires minimal oversight and the ability to drive data workstreams forward quickly.

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 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.
Strong communication skills with the ability to translate technical decisions into client-friendly language.

Nice To Haves

Experience with data quality frameworks and validation rule design
Exposure to fund administration, private capital, or financial services data environments
Familiarity with client onboarding processes or APIs

Responsibilities

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.
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.
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.
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.
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.
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.