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Sr Dir, Partner Success
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C-17 Field Engineer (Experienced or Lead)
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Senior Low Observables Design & Integration Engineer - Pole Model
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Added 7 mins ago
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Mainframe Software Engineer I
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Added 9 mins ago
Senior Space Mission Software Engineer
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Network Engineer
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Senior Azure Cloud Administrator (Remote Available)
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We are seeking a highly skilled Azure Cloud Engineer with strong FinOps expertise and infrastructure operations experience to support a managed services operating model.
Added 10 mins ago
Strategic Mission Systems Engineer
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Network Engineer Principal (TS/Q clearance required)
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Unified Endpoint Management (UEM) Engineer
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Added 10 mins ago
ICAM Software Engineer - SailPoint/Radiant Logic
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Added 10 mins ago
Senior GNC Engineer, Modeling and Simulation
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Schonfeld

Lead Data Content Curation Engineer

NY, United States

The base pay for this role is expected to be between $190,000.00 and $250,000.00.

Apply on job-boards.greenhouse.io About employer

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Job details

Pay: The base pay for this role is expected to be between $190,000.00 and $250,000.00.
Posting language: English
Posted: Nov 10, 2026

Original job description

The Role We are seeking a hands-on Lead Data Content Curation Engineer to transform complex financial and enterprise datasets into trusted, research-ready data products. Working closely with investment research teams, data engineers and platform specialists, you will translate raw vendor content into well-designed models, curated tables, views and reusable datasets across relational databases and modern lakehouse platforms. You will deliver usable data products quickly while progressively introducing AI-assisted curation and automation. This role focuses on data quality, usability and business meaning rather than database infrastructure administration.

What you’ll do

Own end-to-end curation of external and internal datasets, from source evaluation and ingestion design through modeling, validation, publishing and ongoing evolution; prioritize rapid delivery of research-ready content.
Interpret complex financial vendor data, including S&P Capital IQ and comparable fundamental, reference and market datasets: 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 seamless, 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 regardless of where data resides.
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 supporting 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 consistent 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.

What you’ll bring

What you need:

A 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, with an understanding of the underlying financial datasets, instruments, identifiers and research workflows.
Hands-on experience using AI/LLM-powered development tools to accelerate data engineering, with the ability to apply 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.

We’d love if you had:

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.

Who we are Schonfeld Strategic Advisors is a global multi-strategy, multi-manager investment platform that harnesses the transformative power of people to perform in all market environments. Our dynamic culture inspires better outcomes for our team, our investors, and our partners. We aim to consistently deliver risk-adjusted returns, with people driving performance.

We specialize in four core strategies: Quantitative Trading, Fundamental Equity, Tactical Trading, and Discretionary Macro & Fixed Income. We capitalize on inefficiencies and opportunities within the markets, drawing from a significant investment in proprietary technology, infrastructure, and risk analytics.

We invest through internal portfolio managers and external partner funds, pursuing alignment among investors, investment professionals, and the firm. Our footprint spans 7 countries and 19 offices.

Our Culture At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas - at all levels - to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

The base pay for this role is expected to be between $190,000.00 and $250,000.00. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.

__PRESENT

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More about this job

Skills

Research
S&P Capital IQ
Data models
SQL
PostgreSQL
Data engineering
Data modelling
Python
CI/CD
Monitor operations in the leather industry
Financial statements
AWS

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Work requirements

Jobs in the United States