Senior Data Management Professional - Data Engineering - Corporate Actions

Design, build, and optimize scalable data pipelines to ingest, transform, and deliver high-volume financial data using Python, SQL, and modern enterprise data technologies, including workflow orchestration, distributed processing, messaging frameworks, and cloud-based data platforms.

Develop robust data architectures and automated ingestion frameworks that support structured and unstructured data sources, enabling scalable, high-performance data processing, schema design, and seamless interoperability across downstream systems.

Leverage AI, Large Language Models (LLMs), NLP, and machine learning to extract, normalize, and enrich Corporate Actions data (e.g., dividends, stock splits, rights offerings) from issuer filings, regulatory disclosures, news, press releases, exchange feeds, and other complex data sources.

Implement intelligent Human-in-the-Loop (HITL) workflows and data quality frameworks that combine AI-driven extraction with automated validation, business rules, statistical methods, and exception management to maximize accuracy, completeness, and operational efficiency.

Develop monitoring, reporting, and observability solutions by creating data quality dashboards, pipeline health metrics, and SLA monitoring capabilities that provide visibility into data integrity, processing performance, and operational effectiveness.

Partner cross-functionally with Product, Engineering, Data Science, and business stakeholders to design scalable data solutions, standardize engineering best practices, and deliver high-quality data products that support trading, analytics, and client-facing applications.

Bachelor’s Degree or Master’s Degree in Computer Science, Data Engineering, Information Systems, Quantitative Finance, or an equivalent quantitative discipline.

3+ years of hands-on experience in a Data Engineering or technical Data Management role, with a proven track record of building scalable ETL/ELT pipelines in a production environment.

Advanced technical proficiency in Python (Pandas, PySpark, or standard data manipulation libraries) and complex SQL/NoSQL database engineering.

Hands-on experience applying AI/LLMs and Machine Learning (e.g., LangChain, LlamaIndex, AI Assisted APIs, Hugging Face, or custom NLP models) for structured/unstructured document processing and automated information extraction.