Original listing text, shown exactly as published by the company.
What you'll do
Build and Extend Core Data Infrastructure
- Design and ship production-quality enhancements to the data fabric platform, including streaming ingestion pipelines (Kafka, Flink), Redshift-based storage and query layers, and data distribution to consuming teams and products
- Contribute to the self-service ingestion and transformation platform – enabling domain teams to publish data models and analysts to create aggregations without blocking on data engineering
- Extend and maintain the CI/CD pipeline for dbt transformations, including validation frameworks that prevent regressions as the model library grows
Own Data Catalog and Governance Capabilities
- Build and operate the data catalog platform, enabling stakeholders across the company to discover available data, understand business context, search by keyword, and trace lineage
- Implement integrations between the catalog and upstream systems – dbt, query history, data publishers – to keep lineage and metadata accurate and current
- Contribute to governance tooling that ensures data quality, compliance, and observability across the platform as usage scales
Build and Operate with AI-Native Practices
- Use agentic coding tools and LLM-assisted development as your primary workflow – this is how the entire team operates
- Critically evaluate AI-generated code for correctness, edge cases, and regressions before shipping
- Bring and share strong opinions on how to use AI tooling effectively across the full software development lifecycle
Drive Quality, Reliability, and Observability
- Build and maintain monitoring, alerting, and observability tooling that keeps data pipelines healthy and issues detectable before they affect consumers
- Write well-tested, performant code and participate actively in code reviews, raising the technical bar for the team
- Produce clear technical documentation for the systems you build, enabling self-service adoption by internal teams
Collaborate Across Engineering and Product
- Partner with your engineering manager, peer engineers, and data consumers (domain teams, analysts, product engineers) to translate requirements into well-scoped technical work
- Contribute to cross-functional alignment on data contracts, schema standards, and ingestion patterns – helping prevent duplicated logic and siloed implementations across teams
- Develop deep domain knowledge of private markets data – fund administration, investment workflows, reporting requirements – to build infrastructure that serves real business needs
Qualifications
Required• 4–7 years of software engineering experience, with a track record of owning and shipping production data systems end-to-end
- Hands-on experience building and operating data pipelines or data warehouse infrastructure: streaming or batch ingestion, ETL/ELT patterns, schema design, query optimization
- Experience with AWS data infrastructure; Redshift experience strongly preferred
- Comfort working across the data stack – from pipeline logic and storage to APIs and observability tooling
- Production experience building with LLMs – integrating models into real systems, not just experimentation
- Fluency with AI-assisted and agentic development workflows; you use these tools daily and evaluate their output critically
- Strong written communication – able to document technical decisions clearly for both engineering and non-technical audiences
Preferred• Experience with streaming data technologies such as Kafka or Flink
- Experience with dbt (dbt-core preferred) and workflow orchestration tools such as Airflow
- Familiarity with data catalog or data governance platforms – open source (e.g. OpenMetadata) or commercial
- Experience building backend services using Python/FastAPI and designing and implementing scalable RESTful and GraphQL APIs
- Experience authoring modern web applications using ReactJS and TypeScript and designing reusable UI components and scalable frontend architecture.
- Background in financial services data – familiarity with fund administration, investment data schemas, or institutional reporting workflows is a meaningful differentiator
- Experience building self-service data platforms or developer-facing data tooling for internal consumers
•Familiarity with data lineage, data contracts, or metadata management patterns at scale
CompensationCompensation for this position includes a base salary, equity, and a variety of benefits. The U.S. base salary range for this role is $185,000 – $225,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
