Lead Data Content Curation Engineer

Schonfeld • New York, NY

6h • $190,000 - $250,000

About The Position

Schonfeld Strategic Advisors is seeking a hands-on Lead Data Content Curation Engineer to turn complex financial and enterprise datasets into trusted, research-ready data products. The role works closely with investment research teams, data engineers, and platform specialists to translate raw vendor content into well-designed models, curated tables, views, and reusable datasets across relational databases and modern lakehouse platforms. It emphasizes rapid delivery, data quality, usability, and business meaning rather than database infrastructure administration, while progressively introducing AI-assisted curation and automation.

The engineer owns dataset curation from source evaluation and ingestion design through modeling, validation, publishing, and ongoing evolution. The role interprets financial vendor content, builds reusable research datasets and reliable processing pipelines, and establishes controls for accuracy, completeness, freshness, lineage, and point-in-time correctness. It also partners directly with researchers and analysts to turn investment workflows into documented, reusable data products.

Schonfeld is a global multi-strategy, multi-manager investment platform operating across 7 countries and 19 offices. Its core strategies are Quantitative Trading, Fundamental Equity, Tactical Trading, and Discretionary Macro & Fixed Income. The firm describes its culture as collaborative and teamwork-oriented, with learning and educational offerings, opportunities to make an impact, and internal networks, external partnerships, and service initiatives.

The expected base pay range is $190,000 to $250,000. The role may also be eligible for a performance bonus and a competitive benefits package. Actual compensation depends on factors including skills, qualifications, and experience.

Requirements

• Bachelor's degree.
• 10+ years in data engineering, database development, or financial data platform engineering, including 5+ years working with financial datasets in investment management, financial services, or financial data providers.
• Expert SQL, strong relational data modeling, and practical 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; deep expertise in every named platform is not required.
• 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, and historical revisions, including point-in-time correctness and avoidance of look-ahead bias.
• Demonstrated domain expertise in at least one asset class or investment strategy, such as equities, futures, options, fixed income, credit, macro, or statistical arbitrage, and understanding of its financial datasets, instruments, identifiers, and research workflows.
• Hands-on experience using AI/LLM-powered development tools to accelerate data engineering and applying AI-assisted capabilities to repetitive curation and automation tasks.
• Ability to work directly with research users, clarify requirements, and rapidly deliver documented, reusable data products.

Nice To Haves

• Experience with S&P Capital IQ, FactSet, Bloomberg, LSEG, or similar vendor content and licensing-aware data delivery.
• Experience supporting statistical arbitrage, quantitative research, or Research Security Master datasets and their downstream analytical workflows.
• Experience with SingleStore, AWS lakehouses, Apache Iceberg, point-in-time or bitemporal models, slowly changing dimensions, and revision-aware histories.
• Experience building AI agents, LLM-powered data workflows, or intelligent automation for data engineering and curation.
• Experience in a multi-strategy hedge fund, asset manager, investment bank, or financial data provider supporting fundamental equity, quantitative, credit, or event-driven research.
• Familiarity with AWS, DataHub or other data catalogs, data contracts, APIs, and entitlement-aware publishing.

Responsibilities

• Own end-to-end curation of external and internal datasets, from source evaluation and ingestion design through modeling, validation, publishing, and ongoing evolution, prioritizing rapid delivery of research-ready content.
• Interpret complex financial vendor data, including S&P Capital IQ and comparable fundamental, reference, and market datasets, including schemas, identifiers, relationships, update patterns, and methodology changes.
• Design and implement research-ready 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 that support distinct investment workflows without duplicating core definitions, identifiers, or historical logic.
• Enable location-independent discovery and access to trusted datasets through catalog, API, and query services, working with platform teams to preserve consistent semantics, entitlements, and governance across data locations.
• Partner with investment researchers and analysts to translate use cases into reusable content, including point-in-time company fundamentals, entity-linked financial metrics, and historical research datasets.
• Curate and deliver research-ready datasets for fundamental and quantitative investment strategies, including statistical arbitrage, Research Security Master, cross-vendor security identifiers, historical mappings, corporate actions, and point-in-time reference data for research and backtesting.
• Build reliable ingestion and transformation pipelines with incremental processing, CDC where appropriate, backfills, reconciliation, and exception handling.
• Implement controls for completeness, accuracy, freshness, lineage, corporate actions, revisions, and point-in-time correctness; investigate discrepancies with vendors and stakeholders.

Benefits

• The role may be eligible for a performance bonus and a competitive benefits package.
• The firm provides learning and educational offerings and opportunities to make an impact.
• The firm encourages community through internal networks, external partnerships, and service initiatives that promote inclusion and purpose.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior