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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst - **Company:** CAPITAL TOWERS II, INC. - **Location:** New York, NY, 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, Application Integration Architecture, Confluence, JIRA, BigQuery, C++ (Programming Language), Cloud Computing, Computer Programming, Information Engineering, Data Integrity, Data Mining, Data Warehousing, Database Queries, Linux, Dimensional Modeling, Distributed Computing Environment, Information Extraction, Python (Programming Language), Named Entity Recognition, Network File Systems, NumPy, Performance Tuning, DataOps, Search Technologies, SQL Databases, Unstructured Data, Google Cloud, Sql Optimization, Pytorch, Retrieval-Augmented Generation, Large Language Models, Snowflake, Apache Spark, Pandas, Semi-structured Data, Scikit Learn, Information Technology, HuggingFace, Apache Kafka, Data Management, Machine Learning Operations, Video Streaming, Data Delivery, Data Pipelines, Databricks - **Published:** September 18, 2026 - **Apply:** https://www.dice.com/job-detail/5dc8889f-38f9-4a90-96e5-866e785b4460 ## About the Role * 3+ years of professional experience in data engineering, preferably within the financial industry (Hedge Fund, Asset Manager, or FinTech) * Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline * Understanding of financial instruments, financial datasets from Bloomberg, S&P, LSEG * Experience with distributed computing frameworks like Spark or streaming technologies like Kafka * Proficiency in C++, Java, or Rust is a strong plus * 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 * Programming Mastery: Expert-level proficiency in 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 * Ability to evaluate model quality (precision/recall, error analysis, gold-set design), manage failure modes, and ship reliable pipelines rather than one-off prototypes * Familiarity with common AI libraries and APIs (e.g., scikit-learn, PyTorch, Hugging Face, and/or LLM APIs) * Strong hands-on experience with workflow orchestration tools such as Airflow, Dagster, or similar * Proven experience building data platforms on AWS / Google Cloud Platform and modern data warehouses (e.g., Snowflake, BigQuery, DataBricks) * Understanding of dimensional modeling, SCD strategies, data partitioning, and VLDB design principles * Experience with Linux and Windows operating systems, NFS, S3, and work management platforms (JIRA, Confluence, etc.) * Excellent problem-solving skills with a sense of ownership. Ability to communicate technical and AI-related concepts effectively to non-technical traders and researchers, including limitations and risk ## Description * Designing, building, and maintaining scalable batch and real-time data pipelines to ingest, cleanse, and normalize data from a wide variety of structured and unstructured sources (market data, web scrapes, vendors, alternative data) * Designing and productionizing AI/ML workflows for unstructured and semi-structured data, including document/entity extraction, classification, vendor-file parsing, news and filings processing, and alternative-data onboarding * Owning prompt/model selection, evaluation harnesses, human-in-the-loop review, and monitoring so AI-assisted feeds meet the firm's accuracy and latency standards * Owning core investment data domains, designing and evolving data models for Security Masters, Corporate Actions, and Referential datasets across various asset classes (Equities, Futures, FX, Derivatives) * Evaluating and implementing modern data tooling (SQL, Kafka, Airflow, Cloud) to improve the speed, reliability, and observability of the data ecosystem * Implementing robust validation checks, anomaly detection, and reconciliation logic to ensure "zero-error" data delivery to trading systems * Applying statistical and ML-based methods to detect outliers, drift, and silent data breaks * Partnering directly with Data Scientists and Quants to understand their research needs, prototype data extraction methods (including AI-enabled approaches), and operationalize research signals into production-grade feeds * Managing the end-to-end process of onboarding new datasets, engaging with external vendors to understand data nuances, and integrating APIs * Assessing where AI can accelerate mapping, documentation, and QA without compromising data integrity ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Vectorize all the things! 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