Data Team Lead

About Us

Edgestream Partners is a team of scientists, engineers, and investment professionals dedicated to developing quantitative models of financial markets and deploying them in systematic trading strategies continuously across global markets. Our work sits at the intersection of research, technology, and investment management, where rigorous analysis and robust engineering come together to build scalable trading systems.

Our culture has more in common with a research lab or high-growth technology company than with a traditional financial institution. We value intellectual curiosity, creativity, diligence, and integrity. We focus on long-term thinking, investing in ideas and systems that are designed to endure and evolve as markets and technologies change. Collaboration is central to how we work - we believe that an open exchange of ideas and close collaboration across disciplines allow us to build stronger models and better infrastructure that can ultimately empower better outcomes.

Founded in 2003, Edgestream has grown into a firm where thoughtful, high-quality work can have a meaningful and lasting impact. We continue to expand our capabilities and are looking for talented individuals who are excited to contribute to a collaborative research environment and grow with us in the years ahead.

About the Job

Edgestream is seeking an experienced, hands-on data leader to join our engineering team. This is a high-impact role responsible for shaping the architecture and evolution of the firm's data platform across the full lifecycle of our systematic trading and quantitative research systems.

The position offers the opportunity to play a central role in designing and scaling the data infrastructure throughout the firm. The successful candidate will combine architectural leadership with hands-on engineering while working closely with researchers, engineers, and trading teams across the firm.

Responsibilities

* Lead and develop the firm's data engineering team, fostering a results-oriented culture that balances immediate business needs with long-term data strategy.

* Take ownership of critical production data pipelines, maintaining and enhancing them to ensure reliability, performance, and scalability.

* Design and implement the firm's next-generation data platform supporting large-scale market data and heterogeneous financial datasets used in research and trading.

* Optimize data workflows and operational processes, leveraging modern AI and data technologies to improve efficiency and engineering productivity.

* Collaborate closely with trading operations, quantitative researchers, and business teams to assess needs, prioritize initiatives, and plan data platform development.

Required Qualifications

* Bachelor's or Master's degree in computer science, engineering, mathematics, physics, or a related quantitative discipline.

* At least 7 years of experience in financial data management, data strategy, and leadership roles.

* Demonstrated success executing independently in a high-performance and rapidly evolving environment.

* Experience integrating data from financial data vendors and exchange feeds, along with a strong understanding of corporate actions, instrument symbology, and security master design.

* Strong proficiency with modern database systems, including open-source technologies such as Postgre

SQL and cloud-based platforms such as Snowflake, Databricks, or Big Query.

* Strong proficiency and hands-on experience with Python, including experience with libraries such as Pandas, Polars, and Num Py.

* Exceptional attention to detail along with strong organizational and communication skills.

* Proven ability to leverage modern AI/ML tooling effectively to solve complex problems and enhance data workflows.

Preferred Qualifications

While not required, experience in the following areas is a strong plus:

* Deep familiarity with financial market data structures, including equities, futures, and FX datasets, particularly in the context of building historical backfill and replay pipelines for research and backtesting.

* Experience working with No

SQL and vector databases to store, index, and query large-scale semi-structured and unstructured datasets.

* Experience designing and operating large-scale data lake or lakehouse architectures for financial or time-series data, including modern table formats such as Apache Iceberg or Delta Lake.

* Experience building columnar data architectures using technologies such as Parquet or Apache Arrow.

* Strong understanding of partitioning strategies, metadata catalogs, and schema evolution for large-scale datasets.

* Experience building distributed data processing pipelines using frameworks such as Apache Spark.

* Solid understanding of parallel computing, task orchestration, and distributed query execution.

* Experience optimizing large-scale ETL workloads for throughput, latency, and operational efficiency.

Benefits:

* Competitive salary, bonus, and incentive compensation tied to overall firm performance.

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