Senior Data Engineer

Senior Data Engineer

Ready to own the investment data that powers a world-class quantitative trading platform? As a Senior Data Engineer on the Data team, you'll design and build the pipelines, AI/ML workflows, and data models that trading systems depend on for zero-error delivery. This is a role for someone who wants to own core data domains end to end and put modern AI to work on real production problems, not one-off prototypes.

Our client is a leading quantitative trading firm with a 25+ year track record of innovation and a reputation for discovering unique market opportunities. They've built their business on a high-performance platform and independent trading teams, and they're home to some of the world's best systematic trading and engineering talent. If you want to work with highly intelligent, motivated people who push each other to do their best work, without the egos, you'll feel right at home.

Why You'll Love This Role

• Own core investment data domains and shape the data models that power trading across multiple asset classes.
• Put modern AI to work on real production problems, from prompt and model selection to evaluation and monitoring.
• Do work that matters every day. The feeds you build deliver zero-error data straight to trading systems.
• Collaborate in an open, welcoming culture with no unnecessary hierarchy, where great ideas win on merit.

What You'll Do

• Design, build, and maintain scalable batch and real-time pipelines that ingest, cleanse, and normalize data from structured and unstructured sources like market data, web scrapes, vendors, and alternative data.
• Design and productionize AI/ML workflows for unstructured and semi-structured data, including document and entity extraction, classification, vendor-file parsing, and news and filings processing.
• Own core investment data domains, designing and evolving data models for Security Masters, Corporate Actions, and Referential datasets across Equities, Futures, FX, and Derivatives.
• Own prompt and model selection, evaluation harnesses, human-in-the-loop review, and monitoring so AI-assisted feeds meet the firm's accuracy and latency standards.
• Implement robust validation, anomaly detection, and reconciliation logic, and apply statistical and ML-based methods to catch outliers, drift, and silent data breaks.
• Partner directly with Data Scientists and Quants to prototype data extraction methods and operationalize research signals into production-grade feeds.
• Evaluate and implement modern data tooling like SQL, Kafka, Airflow, and Cloud to improve the speed, reliability, and observability of the data ecosystem, and manage the end-to-end onboarding of new datasets and vendor APIs.

What You Bring

• 3+ years of professional data engineering experience, preferably within the financial industry (hedge fund, asset manager, or FinTech).
• A Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field.
• Expert-level Python (including Pandas, NumPy, and async frameworks) and advanced SQL (complex queries, window functions, performance tuning).
• Hands-on production experience applying AI or ML to data problems, such as NLP, information extraction, classification, or LLM-based processing of unstructured data.
• The ability to evaluate model quality (precision/recall, error analysis, gold-set design), manage failure modes, and ship reliable pipelines rather than prototypes.
• Strong hands-on experience with workflow orchestration tools like Airflow or Dagster, and proven experience building data platforms on AWS or GCP with modern warehouses like Snowflake, BigQuery, or Databricks.
• An understanding of financial instruments and datasets from Bloomberg, S&P, and LSEG, plus dimensional modeling, SCD strategies, data partitioning, and VLDB design principles.
• Excellent problem-solving skills, a strong sense of ownership, and the ability to explain technical and AI-related concepts to non-technical traders and researchers, including limitations and risk.

Bonus Points If You Have

• Experience with distributed computing frameworks like Spark or streaming technologies like Kafka.
• Proficiency in C++, Java, or Rust.
• Experience with retrieval-augmented generation, vector search, fine-tuning or distillation, LLM evaluation frameworks, or agentic workflows for data operations.
• Prior work applying AI to financial documents, corporate actions, or alternative data.
• Familiarity with common AI libraries and APIs like scikit-learn, PyTorch, Hugging Face, and LLM APIs.
• Experience with Linux and Windows, NFS, S3, and work management platforms like JIRA and Confluence.

Compensation and Perks

• Base salary: $230,000 to $250,000 depending on experience, plus eligibility for a discretionary bonus.
• Generous paid time off and financial wellness tools.
• Hybrid working opportunities out of a NYC Financial District office.
• Free breakfast, lunch, and snacks daily.
• In-office wellness experiences and reimbursement for select wellness expenses like gym and personal training.
• Company-sponsored sports teams, fitness events, volunteer opportunities, and social events throughout the year.