Sr Engineering Manager, Ontology Platform

SS&C Technologies•Remote - New Jersey, US, MA

1d•$185,000 - $195,000•Onsite

About The Position

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.

Requirements

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 systems.
Experience building on top of a shared/foundational enterprise data platform rather than a fully greenfield stack. You are comfortable navigating platform dependencies, advocating for capability build-out with a platform team you don't directly control, and making build-vs-leverage trade-offs.
Strong communication skills: able to work directly with business, compliance, and senior stakeholders, and to translate between business requirements and engineering execution for both the team and leadership.
Experience in or working with financial services data environments is a plus, given the regulatory, auditability, and data-quality expectations typical of the domain.
Practical, hands-on experience using AI coding agents (e.g., Claude Code, GitHub Copilot, or similar) as a core part of your development workflow, with a track record of using them to meaningfully increase delivery speed and quality, and the ability to instill that same practice across a team.

Nice To Haves

Prior experience building or scaling an enterprise ontology, knowledge graph, or MDM platform from early stages through production maturity, ideally as a founding or early technical leader.
Experience growing a team from a small initial size (e.g., 5-6) as platform scope and scale increase.
Experience with cloud-native architectures and data platforms (e.g., containerized services, managed graph/database offerings, streaming/event-driven pipelines).
Familiarity with data governance and metadata management tooling, and how they integrate with an ontology platform.
Background in financial services, asset management, or another regulated, data-intensive industry.

Responsibilities

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.
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.
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.
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.