Research Engineer

Overview
SummitTX Capital is a multi-manager, multi-strategy hedge fund managing over $3 billion in AUM. Founded in 2015, the firm spun out from Crestline Investors in 2025 to become an independent SEC-registered adviser under the SummitTX Capital brand. We operate an open-architecture platform across Fundamental, Tactical, Quantitative, and Capital Markets strategies, with offices in Fort Worth and New York.

We are seeking a hands-on Research Engineer to help build and scale our systematic data platform that powers alpha research and production signals. You will work end-to-end, from idea generation and data acquisition to model development, backtesting, deployment, and monitoring, with an initial portfolio mix of Long/Short Equity initiatives and Systematic Fundamental research. The role reports to the Head of Data and partners daily with portfolio managers, analysts, the central research team, risk, and operations.

 Key Responsibilities

• Design, build, and maintain systematic data pipelines, including ingestion, medallion-style data modeling, feature engineering, and experiment tracking
• Operationalize robust ELT workflows using DBT/SQL and Python on Databricks, with strong enforcement of data quality, lineage, and documentation
• Develop research-grade datasets and features across market, alternative, and fundamental domains to support L/S Equity and systematic strategies
• Productionize models and alpha signals with CI/CD pipelines, model registries, monitoring, and cost/performance optimization on Databricks and AWS
• Partner with PMs and Analysts to translate investment hypotheses into testable research artifacts, delivering clear results, visualizations, and readouts to guide decision-making
• Contribute to the evolution of the data platform roadmap, including observability, governance, access controls, cataloging, and documentation standards

 Key Objectives for the First 12–18 Months

• Take ownership of initial research datasets, ensuring they are curated, well-documented, and trusted by PMs, Analysts, and the central research team
• Deliver production-grade ELT pipelines in Databricks (DBT/SQL, Python) with automated testing, anomaly detection, and reproducibility standards
• Partner directly with PMs and Analysts to turn investment hypotheses into research artifacts, feature sets, and back tested signals that can progress toward production
• Implement experiment tracking, model registry, and monitoring frameworks to ensure transparency, reproducibility, and performance measurement of models and signals
• Contribute to defining and rolling out governance standards for data cataloging, lineage, and access controls across the research platform
• Optimize compute workflows (Databricks/AWS) to balance performance with cost efficiency, enabling scalable research without infrastructure bottlenecks
• Provide ongoing research readouts, visualizations, and clear communication to investment teams, helping guide decision-making and accelerate alpha discovery

 Qualifications

• BS or MS in Data Science, Statistics, Data Engineering, Applied Math, Computer Science, or related field with strong academic performance
• Strong Python and SQL fundamentals; comfort with Git and testing frameworks
• Coursework or internship experience in data modeling, ETL/ELT, ML/statistics, or time-series analysis
• Clear communication skills and ability to partner with investment, risk, and operations stakeholders

 Preferred

• Hands-on experience with DBT, DuckDB or Spark, and modern data-quality toolkits
• Familiarity with Databricks (Lakehouse, Unity Catalog) and AWS data services (S3, Glue/Athena, Lake Formation)
• Exposure to ML frameworks (pandas, scikit-learn, PyTorch, MLflow) and feature pipelines
• Experience with visualization and BI tools (e.g., Plotly, Tableau/Power BI), and Financial Data Platform (e.g. Bloomberg Terminal)
• Experience in GenAI/LLM applications (prompt engineering, RAG, agentic workflow)

 Tech Stack

• Languages & Frameworks: Python, SQL, DBT, Spark, scikit-learn, PyTorch, MLflow
• Data & Platform: Databricks (Delta Lake, Unity Catalog, Serverless Compute), DBT, AWS (EC2, S3, Athena), DuckDB, Bloomberg Terminal
• Tooling & Ops: GitHub/Bitbucket, Databricks Lakeflow, Airflow, CI/CD pipelines, observability frameworks, Linux, VS Code, Cursor

Compensation

• Base Salary Range: $90k to $125k per year, contingent on experience
• Discretionary annual bonus eligible