Evolve logical and physical data models that create a comprehensive view of user flows, system dependencies, resiliency signals, and risk measures, and develop new models that support prediction and decisioning where appropriate
Translate business, risk, and control requirements into implementable technical designs and a pragmatic delivery plan, partnering with architects, data engineers, analysts, and stakeholders across a matrix organization. You will contribute to the broader data architecture strategy that underpins resiliency analytics and risk modeling, including integration and interoperability across data sources and systems
Implement and continuously improve data quality management, metadata management, and data governance practices to increase reliability, explainability, and auditability, and enable data lineage and traceability across sources, transformations, and curated outputs
Work with modern architectures and patterns (including microservices, event-driven designs, cloud-based data platforms, and Lambda/Kappa patterns) to support scalable and, where needed, near real-time data requirements (per provided job specifications).
Leverage SQL heavily and apply a strong understanding of NoSQL and other database technologies, managing and optimizing databases for performance and efficiency
Follow embed automation and engineering best practices (version control, CI/CD, code review, testing, and documentation) to improve stability and delivery, and use advanced developer tooling to accelerate delivery while operating within firm standards and control requirements
Need to have Modern tooling expectations for this role include: Python programming for data engineering, orchestration, automation, and developer productivity, GitHub Copilot for assisted development, subject to firm approval, policy, and applicable control requirements and Claude Code for assisted development, subject to firm approval, policy, and applicable control requirements
Required Qualifications, Capabilities, and Skills
5+ years of relevant experience in data engineering, analytics engineering, or data platform engineering roles, with demonstrated delivery across the data lifecycle from collection through transformation, modeling, and analytics enablement
Strong proficiency in SQL, hands-on programming experience in Python, and experience with data query paradigms including SQL and NoSQL;
Practical experience with data modeling, data integration/ETL processes, and interoperability across multiple business systems, including data migration and mapping complex relational data between systems
Experience with database technologies such as PostgreSQL, MySQL, and MongoDB, including performance optimization and operational management
Familiar with big data and analytics engines/platforms such as Apache Spark and Hadoop, and with open-source analytics/query engines for big data
Experience implementing, or partnering closely on, data quality, metadata, and governance controls that increase reliability and auditability
Understand modern distributed systems patterns including APIs and distributed event streaming, and can operate effectively in cloud-based and event-driven environments
Demonstrate strong analytical and problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a matrix environment, with effective communication skills to build partnerships across business and technology stakeholders
Preferred Qualifications, Capabilities, and Skills
Familiarity with GraphQL is a plus
A degree (or equivalent practical experience) in Computer Science, Information Systems, Data Science, or a related field is preferred (per provided job specifications). Experience with scenario generation and modeling approaches that support resiliency and recovery risk analysis is preferred, particularly where outputs must be explainable and operationally actionable for control stakeholders (per provided job specifications, supplemented with role intent).
Exposure to statistical and analytical techniques and data science methods, including familiarity with data mining techniques, is preferred (per provided job specifications). Experience producing high-quality data architecture artifacts—such as target-state diagrams, data flows and lineage views, and conceptual/logical models—consumable by a broad stakeholder group is also preferred (per provided job specifications). Industry accreditation such as TOGAF or cloud/solution architecture certifications is a plus
