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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** CATCH Inc. - **Location:** United States - **Experience:** Expert - **Salary:** $230,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Microsoft Windows, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Algorithmic Trading, Amazon Web Services, Amazon S3, JIRA, BigQuery, C++ (Programming Language), Cloud Computing, Cyber Security, Computer Literacy, Information Engineering, Data Files, Data Mining, Data Security, Dataspaces, Database Queries, Linux, Dimensional Modeling, Distributed Computing Environment, Distributed Systems, Information Extraction, Python (Programming Language), Natural Language Processing, Named Entity Recognition, Network File Systems, NumPy, Performance Tuning, Systems Development Life Cycle, DataOps, Search Technologies, SQL Databases, Unstructured Data, Workflow Management Systems, Data Processing, Scripting, Sql Optimization, Pytorch, Retrieval-Augmented Generation, Large Language Models, Snowflake, Apache Spark, Pandas, Build Management, Semi-structured Data, Core Data, Scikit Learn, Information Technology, HuggingFace, Atlassian Tools, Apache Kafka, Data Management, Machine Learning Operations, Video Streaming, Databricks - **Published:** September 22, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-engineer-ny--f918b1cd-7c48-412f-af0e-cd30f4b2eec0 ## About the Role * 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., Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Analysis Skills, Apache Kafka, Apache Spark, Application Programming Interface (API), Artificial Intelligence (AI), Asset Management, Atlassian JIRA, Business Process Outsourcing, C++ Programming Language, Cloud Computing, Computer Science, Computer Skills, Consulting, Contract Processing, Data Modeling, Data Partitioning, Data Processing, Data Science, Data Sets, Derivatives, Digital Media, Dimensional Modeling, Distributed Computing, Ecosystems, Engineering, Finance, Fitness, Futures, GCP (Good Clinical Practices), Hedge Funds, Information/Data Security (InfoSec), Java, Linux Operating System, Machine Tool, Mathematics, Microsoft Windows Operating System, NFS (Network File System), Natural Language Processing (NLP), Onboarding, Operations Research, Performance Tuning/Optimization, Physics, Problem Solving Skills, Professional Services, Prototyping, Python Programming/Scripting Language, Reconciliation, Recruiting/Staffing Agency, Risk, SQL (Structured Query Language), Scalable System Development, Sports, Streaming Technology, Structured Data, Technical Recruiting, Technical Research, Trading Systems, Unstructured Data, VLDB, Vendor/Supplier Selection, Warehousing ## Description 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., * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Vectorize all the things! 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