Sr Engineering Manager, Ontology Platform

SS&C is a leading provider of mission-critical, AI-powered technology and services empowering financial services and healthcare organizations to work smarter, faster, and securely. Founded in 1986, SS&C is headquartered in Windsor, Connecticut, and has offices worldwide. More than 23,000 financial services and healthcare organizations, from the world's largest companies to small and mid-market firms, rely on SS&C for expertise, scale, and technology. Job Description Senior Engineering Manager, Ontology Platform About the Team: We are seeking a hands-on Senior Engineering Manager to build and lead a team (initially 5-6 engineers) responsible for architecting, building, and operating our enterprise ontology platform. This is a hands-on management role: you will manage and grow the team, set technical direction, and remain deeply hands-on in architecture and implementation decisions rather than operating purely at a planning/oversight level. You'll drive the build-out of a platform that ingests data from a wide variety of source systems, maps it to a canonical ontology, surfaces discrepancies ("breaks") between source data and ontology definitions, and gives business and technical users configurable tools to manage those mappings and exceptions, all in a financial services data environment where scale, correctness, and auditability matter. The ontology solution is built on top of the enterprise's foundational data platform, so close partnership with that platform team is central to this role. Day to Day: Team Leadership & People Management Build, lead, and grow a team of 5-6 engineers, including hiring, performance management, career development, and day-to-day people leadership. Set team structure, priorities, and delivery roadmap in partnership with product/business stakeholders, balancing ontology/mapping work, platform engineering, and production support. Establish team engineering culture and practices: code review standards, on-call/support rotation, sprint or delivery cadence, and technical documentation habits. Stay hands-on: review and contribute to architecture, participate in code reviews, and step into implementation directly on critical or complex components, especially in the platform's early stages. Represent the team and platform roadmap to senior stakeholders, translating engineering trade-offs and capacity into terms business leaders can act on. Set the expectation and standard for agentic SDLC adoption across the team, ensuring engineers use AI coding agents as a core part of their workflow rather than an optional experiment, and lead by example in your own hands-on work. Platform Engineering & Architecture Own the core platform architecture: services, APIs, and data pipelines that ingest source-system data, apply mapping/transformation logic, and persist it against the enterprise ontology at scale. Drive technical decisions on storage and query layers (graph databases, relational stores, or hybrid approaches), including schema/graph modeling, indexing strategy, and performance tuning as data volume and source count grow. Guide the build of the configuration layer of the product itself, the mechanism by which business and technical users define mappings, rules, and exception-handling logic without requiring a code change per source or use case (e.g., a rules engine, mapping DSL, or admin UI backed by well-designed APIs). Ensure the team ships production-quality code for core platform components: ingestion connectors, mapping/transformation logic, rule/validation engines, and reconciliation ("break detection") pipelines. Establish engineering practices for the platform: version control and branching strategy for ontology/schema changes, automated testing (unit, integration, data quality), CI/CD pipelines, and observability (logging, metrics, alerting) for a production data platform. Design for extensibility from day one; new source systems, new business domains, and new rule types should be addable through configuration and well-defined extension points, not one-off engineering work. Adopt and champion an agentic SDLC approach in your own hands-on contributions, using AI coding agents and related tooling to substantially multiply your engineering throughput, targeting materially higher output than traditional development approaches, not incremental gains. Ontology & Data Mapping Define and evolve the enterprise ontology (entities, relationships, attributes, hierarchies) and translate that model into concrete schemas, graph structures, or data contracts that the platform enforces. Lead the technical implementation of source-to-ontology mappings, reconciling differing definitions, granularity, and nuances across systems in code and configuration, not just documentation. Direct the design and implementation of the rules/validation framework that detects breaks between source data and ontology expectations, including how breaks are surfaced, triaged, and resolved within the product. Stakeholder Partnership Leverage the capabilities of the enterprise's foundational data platform as the underlying infrastructure for the ontology solution, rather than duplicating capabilities that already exist there. Build synergies between the ontology platform and the foundational data platform, identifying where existing platform capabilities (storage, pipelines, compute, governance tooling, etc.) can be extended or reused rather than built bespoke. Proactively identify capability gaps between what the foundational data platform provides and what the ontology solution requires, and partner directly with the data platform team to prioritize and resolve those gaps. Collaborate with the data platform team on shared roadmap items, feature build-out, and design decisions that affect both platforms, acting as the ontology platform's technical voice in that partnership. Partner with business stakeholders, data governance, and compliance teams to translate business/regulatory requirements into concrete engineering and product decisions. Manage technical debt and platform evolution over time as new use cases, sources, and scale demands are added. Minimum Qualifications: 8-12 years of software/data engineering experience, including experience designing and building production-grade platforms or services (not primarily analytics/reporting work). Prior experience as an Engineering Manager or equivalent, with direct people-management responsibility (hiring, performance management, career development) for a team of engineers, not solely a tech-lead role without formal management authority. Strong software engineering fundamentals: proficient in at least one modern backend language (e.g., Python, Java, Scala, Go), API design, service architecture, and writing testable, maintainable production code, and comfortable staying hands-on despite management responsibilities. Demonstrated experience designing and implementing data modeling/mapping frameworks, ideally including ontology, knowledge graph, or master-data-style canonical models, and translating conceptual models into working schemas or graph structures. Practical experience with graph databases and/or semantic web standards (RDF, OWL, SPARQL) or equivalent experience with alternative modeling technologies, plus the judgment to choose the right tool for the problem rather than defaulting to one paradigm. Experience building configurable, multi-tenant-style platforms or internal products where business users define behavior (rules, mappings, workflows) without engineering intervention, e.g., rules engines, low-code configuration layers, or plugin/connector architectures. Solid database and data engineering skills across relational and non-relational systems, including experience with data pipelines, ETL/ELT, and handling large-scale, heterogeneous data sources. Experience with modern engineering practices: CI/CD, automated testing, infrastructure-as-code, observability/monitoring for production syste