Senior Data Engineer – Data Proposition Products

Gen 2 Careers•Denver, CO

1d•Hybrid

About The Position

Gen II is launching multiple data proposition products, aiming to extract clean, validated, and normalized data from fund administration platforms and financial statements. This data will be made available to clients and internal teams as a trusted asset. The goal is to create a scalable data offering, differentiating Gen II in the market through our Sensr product, an analytics portal that simplifies client integration. Strategically, this role will lead the architecture of new data products built on this foundation, collaborating with product and go-to-market teams to commercialize platform data into revenue-driving offerings. The Senior Data Engineer will own the extract → validate → normalize → store pipeline, ensuring data trustworthiness, documentation, and ease of consumption. AI tooling is expected to be used naturally in daily work for tasks like generating transformation logic, scaffolding applications, 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.
Experience thinking about data as a product; designing schemas, documentation, and access patterns for external or cross-team consumers.
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) to ensure data integrity at ingestion.
Monitoring and alerting data quality issues before normalized assets reach clients.
Documenting validation rules and exceptions for client understanding.
Building and maintaining the Medallion architecture (Bronze, Silver, Gold layers) in Snowflake to create curated datasets aligned with Gen II's core data model.
Enabling multi-channel data delivery through the Sensr Portal's Analytics & Databridge, and powering internal analytics, reporting, and AI-driven services.
Building Snowflake Streams and Tasks for incremental processing to keep normalized datasets fresh.
Building Streamlit applications for client-facing data access (dashboards, export tools, data validation status, usage metrics).
Creating and maintaining data dictionaries and lineage documentation for client onboarding and trust.
Collecting client feedback on data quality, schema design, and access patterns for continuous improvement.
Using AI tooling (LLMs, Claude) for generating dbt rules, transformation SQL, Streamlit scaffolding, test cases, and documentation.
Building reusable, metadata-driven patterns for validation, transformation, and deployment to scale the pipeline.
Taking data workstreams from requirement to production with minimal hand-holding.
Working closely with product, integration, and client success teams to understand client data needs and delivery methods.
Collaborating with the Head of Data Product to ensure clean data flow from source to client delivery.
Contributing to data governance practices (lineage, cataloguing, access control, quality standards).