Data and AI Engineer Equities Technology

Skills

About the Role

You will design, build, and maintain scalable data and AI-enabled platforms for equities. You will develop data pipelines, ETL/ELT frameworks, AI services, RAG workflows, and Azure-native solutions. You will productionize AI proofs of concept, implement CI/CD, monitor systems, resolve defects, document technical designs, and collaborate on requirements and delivery.

Requirements

• Bachelor’s degree in information technology or a related field
• 4–6+ years of hands-on experience with Databricks, Spark, Azure Data Factory, Azure Synapse, Python, ADLS, and Azure Functions
• Experience designing and managing Synapse and Azure Data Factory pipelines, activities, and linked services
• Ability to build full and incremental data loads from Azure and on-premises data sources
• Experience designing reusable ETL/ELT frameworks and orchestrating pipelines across ADF, Synapse, and Databricks
• Experience implementing LLM-enabled or RAG-based production solutions
• Proficiency with REST APIs, data gateways, and third-party integrations
• Strong SQL skills, data modeling experience, analytical storage knowledge, and performance-tuning experience
• Experience with Azure DevOps and YAML-based CI/CD pipelines
• Familiarity with Azure Key Vault, automation runbooks, Logic Apps, and cloud security best practices
• Experience with Azure AI Services, including OpenAI and embeddings, is preferred
• Familiarity with Microsoft Fabric is preferred
• Proficiency in ASP.NET, .NET Core, or C# is preferred
• Financial services, capital markets, or regulated-environment experience is preferred

Responsibilities

• Design, build, and maintain scalable data pipelines using Databricks, Azure Data Factory, and Azure Synapse
• Implement ETL/ELT workflows for structured and unstructured financial data
• Ensure data quality, lineage, governance, security, and observability across pipelines and storage layers
• Design and optimize data models and analytical schemas
• Build reusable ingestion and transformation frameworks for analytics and AI workloads
• Build and deploy AI-enabled services, agents, and workflows for equity research, trading, sales, and client service
• Implement LLM-based and agentic patterns, including Retrieval-Augmented Generation
• Integrate AI capabilities through APIs, batch jobs, and event-driven workflows
• Productionize AI and data science proofs of concept into secure, scalable, monitored services
• Optimize prompts, embeddings, orchestration logic, and inference workflows
• Establish monitoring, alerting, lifecycle management, and runbooks for AI solutions
• Support migration of legacy data and application solutions to Azure-native architectures
• Implement CI/CD pipelines using Azure DevOps and YAML
• Use Azure services to build secure, reliable, maintainable solutions
• Develop operational dashboards for pipeline health, SLAs, performance, and cloud spend
• Define functional and technical requirements with stakeholders
• Participate in Agile ceremonies, sprint planning, and retrospectives
• Lead testing, analyze issues, and resolve defects
• Document technical designs, integrations, playbooks, and runbooks
• Monitor industry trends and recommend aligned technology adoption

Benefits

• Discretionary annual bonus and/or commission-based incentives
• Medical, dental, and vision coverage
• Employer-paid short-term and long-term disability insurance
• Employer-paid life insurance
• 401(k)
• Profit sharing
• Paid time off
• Maven family and fertility benefit
• Parental leave, including adoption, surrogacy, and foster placement
• Hybrid work schedule