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Company Overview
hermeneutic research is a best-in-class proprietary trading firm. It deploys research-driven discretionary and systematic strategies as well as makes strategic long-term investments. The partners' decade-long history of success in trading and business building and a firm-wide cultural emphasis on alpha generation, open debate, relentless iteration, and teamwork are key to the firm's continued expansion in a challenging market environment that has hamstrung competitors. A hard-wired emphasis on risk management and opportunistic market participation ensures that hermeneutic research will continue its growth trajectory in the coming decades.
Job Overview
We are looking for a Senior Data Engineer to help us architecting, implementing and operating the complete data infrastructure pipeline for our Research and Trading operations. This role will be crucial in building a scalable, reliable, and cost-efficient system for handling vast amounts of market trading data, real-time news feeds and a variety of internal and external data sources. The ideal candidate will be a hands-on professional who understands the entire data lifecycle and can drive innovation while collaborating across research and engineering teams to meet their needs.
Key Responsibilities
Design, build, and optimize scalable pipelines for ingesting, transforming, and integrating large-volume datasets (market data, news feeds and various unstructured data sources).
Ensure data quality, consistency, and real-time monitoring using tools like DBT, 3rd party libraries that can facilitate data validation processes.
Develop processes to normalize and organize our data warehouse for use across different departments.
Apply advanced data management practices to ensure the scalability, availability, and efficiency of data storage.
Ensure the infrastructure supports trading and research needs while maintaining data integrity, security, and performance at scale.
Collaborate with research and analytics teams to understand their data needs and build frameworks that empower data exploration, analysis, and model development. Create tools for overlaying data from multiple sources.
Ensure that data storage, processing, and management are done in a cost-effective manner, optimizing both hardware and software resources. Implement solutions that balance high performance with cost control.
Stay ahead of the curve by continuously evaluating and adopting the most suitable technologies for the organization’s data engineering needs. Ensure that company’s systems align with the latest best practices in data management.
Requirements
Must-Haves
Strong problem-solving and analytical thinking
Clear communication skills for cross-functional collaboration
Proficiency in building robust data quality checks for ingested data
Experience identifying anomalies in ingested data.
Strong proficiency in writing complex SQL (and similar) queries and optimize performance.
Proficiency in Python or Java / Scala.
Experience building and maintaining complex ETL pipelines with tools like Apache Airflow, dbt, or custom scripts.
Strong understanding of dimensional modeling, star/snowflake schemas, normalization/denormalization principles, etc.
Proven experience with platforms like Snowflake , Redshift , BigQuery , Synapse , etc.
Expert knowledge of Apache Spark , Kafka , Flink , or similar.
Strong understanding of data security and privacy standards
Preferred Qualifications
A degree in Computer Science, Engineering, Mathematics, or a related field.
Familiarity with one of the major cloud platforms ( AWS , GCP , Azure ) and their data services (e.g., BigQuery, Redshift, S3, Dataflow, etc.), proven by certifications (e.g., Google Professional Data Engineer, AWS Big Data Specialty or Snowflake’s SnowPro Data Engineer )
Experience with data quality frameworks (e.g., Great Expectations, Deequ or others)
Experience with Git/GitHub or similar for code versioning.
Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Exposure to containerization/orchestration (Docker, Kubernetes).
Familiarity with data governance , data lineage , and catalog tools (e.g., Apache Atlas, Amundsen).
Hands-on with observability and monitoring tools for data pipelines (e.g., Monte Carlo, Datadog).
Knowledge of machine learning pipelines.
Prior experience in a trading or financial services environment.
Interview Process
CV Screening – We will review your application based on the qualifications and experience outlined above.
HR Interview – To understand your experience, overall skill set, and cultural fit.
Technical Interview – Interviews with multiple stakeholder teams to assess your skill set and cross-functional collaboration.
CTO Interview – Interview with the CTO to discuss your experience, technical expertise, problem-solving approach, and overall fit for the role.
Co-Founder Interview – A concluding discussion to evaluate cultural fit, strategic alignment, and how you resonate with the firm’s values.
Company Values
Throughout the process, you'll be assessed for cultural fit through our company values:
Drive – We believe the best team members are deeply passionate about what they do. That passion fuels their growth, drives them to seek out the best teams, and leads them to hold high expectations of themselves in pursuit of excellence.
Ownership – We aim to extend ownership as broadly as possible across the firm. In return, we value people who take initiative, step up when needed, and treat the company’s goals as their own.
Judgment – We value those who see the big picture and focus on what truly drives impact and results. They use time wisely, adapt across domains, and always prioritize outcomes over comfort zones.
Openness – We foster a culture of open communication, mutual challenge, and shared growth. We believe constructive debate and proactive knowledge sharing lead us closer to the truth—and better decisions.
Competence – We work with people who bring exceptional intellectual strength. What sets them apart isn’t just what they know—it’s how they use what they know to navigate change, adapt quickly, and contribute meaningfully in fast-changing environments.
Resilience – We perform under pressure. Markets move fast, and so do we—but we stay grounded, focused, and calm. We embrace uncertainty, learn from setbacks, and adapt quickly without losing sight of long-term goals.