Data Engineer-Finance

Data Connectivity Lead Key Responsibilities: • Define and execute the product roadmap for AI tooling and data integration initiatives, driving products from concept to launch in a fast-paced, Agile environment. • Translate business needs and product strategy into detailed requirements and user stories. • Collaborate with engineering, data, and AI/ML teams to design and implement data connectors that enable seamless access to internal and external financial datasets. • Partner with data engineering teams to ensure reliable data ingestion, transformation, and availability for analytics and AI models. • Evaluate and work to onboard new data sources, ensuring accuracy, consistency, and completeness of fundamental and financial data. • Continuously assess opportunities to enhance data coverage, connectivity, and usability within AI and analytics platforms. • Monitor and analyze product performance post-launch to drive ongoing optimization and inform future investments. • Facilitate alignment across stakeholders, including engineering, research, analytics, and business partners, ensuring clear communication and prioritization. Basic Qualification: • Bachelor's degree in Computer Science, Finance, or related discipline. MBA/Master's Degree desired. • 5+ years of experience in a similar role. Required Qualifications: • Strong understanding of fundamental and financial datasets, including company financials, market data, and research data. • Proven experience in data integration, particularly using APIs, data connectors, or ETL frameworks to enable AI or analytics use cases. • Familiarity with AI/ML data pipelines, model lifecycle, and related tooling. • Experience working with cross-functional teams in an Agile environment. • Strong analytical, problem-solving, and communication skills with the ability to translate complex concepts into actionable insights. • Prior experience in financial services, investment banking, or research domains. • Excellent organizational and stakeholder management abilities with a track record of delivering data-driven products. Preferred Qualifications: • Deep understanding of Python, SQL, or similar scripting languages. • Knowledge of cloud data platforms (AWS, GCP, or Azure) and modern data architectures (data lakes, warehouses, streaming). • Familiarity with AI/ML platforms. • Understanding of data governance, metadata management, and data security best practices in financial environments. • Experience with API standards (REST, GraphQL) and data integration frameworks. • Demonstrated ability to partner with engineering and data science teams to operationalize AI initiatives.