1 day ago
Base Salary
$190k - $250k/yr
Responsibilities
- Own end-to-end curation of external and internal datasets, including source evaluation, ingestion design, modeling, validation, publishing, and ongoing evolution.
- Interpret complex financial vendor content, including schemas, identifiers, relationships, update patterns, and methodology changes.
- Design relational and lakehouse data models, SQL transformations, curated tables, materialized datasets, and performant views across PostgreSQL, SingleStore, and Apache Iceberg-based platforms.
- Build shared, strategy-agnostic data products supporting investment workflows without duplicating core definitions, identifiers, or historical logic.
- Enable dataset discovery and access through catalogs, APIs, and query services while preserving consistent semantics, entitlements, and governance.
- Partner with investment researchers and analysts to translate use cases into reusable research content.
- Deliver datasets for fundamental and quantitative strategies, including statistical arbitrage, Research Security Master, cross-vendor security identifiers, historical mappings, corporate actions, and point-in-time reference data.
- Build ingestion and transformation pipelines with incremental processing, CDC, backfills, reconciliation, and exception handling.
- Implement controls for completeness, accuracy, freshness, lineage, corporate actions, revisions, and point-in-time correctness.
- Investigate data discrepancies with vendors and stakeholders and apply AI-assisted capabilities to repetitive curation and automation tasks.
Requirements
- Bachelor's degree required.
- 10+ years of experience in data engineering, database development, or financial data platform engineering, including 5+ years working with financial datasets.
- Expert SQL, strong relational data modeling, and experience building analytical views and performant data access patterns in PostgreSQL or comparable databases.
- Strong Python and production pipeline engineering skills, including testing, version control, CI/CD, and operational monitoring.
- Strong understanding of relational and modern lakehouse architectures, SQL-based access, and open table formats such as Apache Iceberg.
- Experience interpreting messy third-party datasets, resolving identifiers and relationships, managing historical changes, and validating data at scale.
- Strong knowledge of financial reference and entity data, company fundamentals, financial statements, estimates, historical revisions, point-in-time correctness, and avoidance of look-ahead bias.
- Domain expertise in at least one asset class or investment strategy, such as equities, futures, options, fixed income, credit, macro, or statistical arbitrage.
- Hands-on experience using AI/LLM-powered development tools for data engineering and curation automation.
- Ability to work directly with research users, clarify requirements, and rapidly deliver documented, reusable data products.
- Preferred experience with S&P Capital IQ, FactSet, Bloomberg, LSEG, or similar vendor content and licensing-aware delivery.
- Preferred experience supporting statistical arbitrage, quantitative research, or Research Security Master datasets.
- Preferred experience with SingleStore, AWS lakehouses, Apache Iceberg, point-in-time or bitemporal models, slowly changing dimensions, and revision-aware histories.
- Preferred experience building AI agents, LLM-powered data workflows, or intelligent automation for data engineering and curation.
- Preferred experience in a multi-strategy hedge fund, asset manager, investment bank, or financial data provider.
- Familiarity with AWS, DataHub or other data catalogs, data contracts, APIs, and entitlement-aware publishing.
Benefits
- Learning and educational offerings are provided.
- The company encourages internal networks, external partnerships, and service initiatives supporting inclusion and purpose.
- The role may be eligible for a competitive benefits package and performance bonus; compensation figures are excluded here.
Tech Stack
Categories
Data Engineering
About Schonfeld
Schonfeld is a privately held, multi-manager hedge fund that allocates capital to internal and external portfolio managers across quantitative, fundamental equity, tactical trading, and discretionary macro/fixed-income strategies. The firm manages money for institutional and other sophisticated investors and supports teams with proprietary technology, infrastructure, and risk analytics. Founded in 1988 and headquartered in New York, it operates globally with an investing footprint spanning five continents.