Equities Research Associate

Bridgewater Associates LP•New York, NY

1d•$150,000 - $200,000•Hybrid

About The Position

This role sits in our Asia Strategies department, whose goal is to continue our growth down the journey of being the premier investment management firm in Asia. This entails developing great investment strategies reflecting our expertise, generating alpha in the markets, researching and publishing our understanding of the macroeconomic environment, and designing solutions to help our clients invest across the region. We are seeking a Data Engineer to join our systematic China equity team. In this hands-on, embedded role, you will work side by side with senior researchers in a fast-paced investment environment, building the data infrastructure and pipelines that support cutting-edge systematic research and investment. Unlike centralized data platform roles, this position is deeply integrated with the investment process. You will own the end-to-end data lifecycle that powers alpha research, portfolio construction, and live trading – from sourcing and ingestion to profiling, validation, transformation, and delivery into research and production systems. You will collaborate with our quantitative researchers, developers, and senior investors, and have direct impact on the data and systems that drive investment decisions.

Requirements

1–5 years of experience as a Data Engineer or in a closely related role, either: embedded with systematic investment teams (hedge fund, asset manager, bank), or in a high-scale, data-intensive technology environment (e.g., consumer, payments, or platform companies).
Strong programming skills in Python and SQL; experience building production-quality, maintainable data pipelines.
Fluency in Mandarin required
Experience working with modern data platforms (e.g., Snowflake or similar cloud data warehouses).
Familiarity with distributed processing and workflow orchestration (e.g., Spark, Airflow, or equivalents).
Proven ability to reason about data correctness, lineage, versioning, and reproducibility in environments where data errors have material downstream impact.
Comfort using lightweight statistical analysis and data science techniques to assess data quality, coverage, and suitability for research use.
Demonstrated experience working with high-dimensional, messy, and evolving datasets, including financial market and reference data (e.g., prices, fundamentals, corporate actions), or large-scale behavioral, transactional, or event-driven data with complex schemas and quality challenges.
Familiarity with the Chinese equity market, including its market structure and key data sources (e.g., Wind), or broader investment landscape.
Experience navigating Chinese data realities, including jurisdiction-specific macro definitions, country-specific corporate structures, and uneven disclosure and historical coverage.
Humility, innate curiosity, and openness to new ideas and approaches.
Driven, confident, and goal-oriented.
Honest, exceptionally direct, and eager to provide and receive objective feedback.
Constantly strive for self-improvement through feedback and self-reflection and are committed to the pursuit of excellence.

Nice To Haves

Care deeply about building high-quality, trustworthy data that directly enables better investment decisions.
Enjoy taking end-to-end ownership of data assets—from evaluating new data sources to designing, maintaining, and evolving production datasets.
Are intellectually curious and seek to understand not just the data itself, but the business and investment concepts it represents.
Enjoy solving ambiguous problems where data definitions evolve, requirements change, and thoughtful judgment is required.
Are a strong collaborator who can work effectively with researchers, engineers, product teams, and external data providers to improve data quality and capabilities.
Think in systems, balancing immediate research needs with scalable, maintainable solutions that will stand the test of time.
Are detail-oriented and hold a high bar for data quality, reliability, and correctness.
Are proactive, self-directed, and comfortable operating in a fast-paced environment where priorities can evolve quickly.
Are eager to learn, embrace feedback, and continuously deepen your technical and domain expertise.
Are excited by the opportunity to have a direct impact on a systematic investment process through the quality of the data that powers it.
Are curious about alpha research and excited to build foundational knowledge. Over time, there may be opportunities to expand into alpha development for those who are interested.

Responsibilities

Identify, evaluate, and onboard new datasets that can enhance our investment process, assessing their quality, coverage, and long-term value.
Own the end-to-end lifecycle of key investment datasets, from ingestion and modeling through ongoing maintenance, documentation, and evolution as research needs change.
Develop and maintain robust, scalable data models, ontologies, and schemas that accurately represent investment concepts and enable consistent downstream use.
Design, build, and maintain scalable data pipelines supporting systematic China equity research and production workflows.
Partner closely with researchers, quantitative developers, and engineering teams to translate evolving research requirements into reliable, maintainable data assets.
Profile and interrogate datasets to understand distributions, coverage gaps, stability over time, structural breaks, and other characteristics that affect research outcomes.
Implement data quality checks, anomaly detection, and monitoring to ensure production datasets are accurate, timely, and complete.
Collaborate with data providers and internal stakeholders to resolve data quality issues, clarify business definitions, and drive enhancements that improve the usefulness of our data.
Build and maintain relationships with data vendors, staying informed on new products and capabilities through conferences, research, and regular engagement.
Contribute to improvements in tooling, standards, and best practices that increase research velocity and strengthen the team's data ecosystem.