Sr Engineer Ontology Platform

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

1d•$165,000 - $175,000

About The Position

We are seeking a Senior Engineer to join the Ontology Platform team, reporting to the Ontology Platform Lead, and covering the full engineering lifecycle of the ontology platform: from stewarding the ontology model and building reconciliation/rules logic, through connector and platform engineering, data and warehouse work, and production operations. This is a hands-on, individual-contributor role for someone who wants broad ownership across the platform rather than a narrowly scoped specialization. The ontology platform is built on top of the enterprise's foundational data platform (Airflow, Iceberg, Spark, Flink, Kafka, Nessie, Trino, StarRocks), so you will leverage that infrastructure rather than rebuilding it. Your focus is the layer above it: translating business ontology definitions into concrete structures, building the connectors and mapping/rules logic that bring source systems onto the platform, maintaining the ontology-specific data model and pipelines, and operating the platform in production, including onboarding new sources and supporting business users day to day.

Requirements

5-9 years of software/data engineering experience, including exposure to data modeling, data pipelines, and production support for a data platform or similar system.
Comfortable working across the full lifecycle of a feature or capability: design conversations with business stakeholders, implementation, deployment, and ongoing operational support, rather than specializing in one slice.
Solid data engineering fundamentals: SQL, ETL/ELT patterns, data modeling (relational and/or graph), and experience building or maintaining data pipelines at scale.
Experience building integrations or connectors to external/source systems, including handling schema variation and data quality issues at the source.
Ability to translate ambiguous business requirements or definitions into concrete technical structures, and to communicate clearly with both business stakeholders and engineers.
Experience with production operations: monitoring, incident response, and troubleshooting for data systems.
Comfortable working on top of a shared foundational data platform (rather than owning core infrastructure), navigating platform dependencies and escalating capability gaps rather than building around them.
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, not just occasional use.
Experience in or working with financial services data environments is a plus, given the regulatory and data-quality expectations typical of the domain.

Nice To Haves

Prior exposure to ontology, knowledge graph, or master data management (MDM) concepts.
Experience with rules engines or validation/reconciliation frameworks.
Familiarity with graph databases or semantic web standards (RDF, OWL, SPARQL).
Experience onboarding new data sources or users onto a platform as a repeatable, documented process.
Familiarity with the underlying foundational data platform's technologies (Airflow, Spark, Kafka, Iceberg, Trino, or similar), even if you are not building that infrastructure directly.
Experience mentoring others on effective use of AI coding agents or agentic development workflows.

Responsibilities

Partner with business stakeholders to translate their ontology definitions (entities, relationships, attributes) into concrete, implementable structures within the platform, without redefining what the business itself owns.
Act as a steward of ontology consistency: identify overlapping or conflicting definitions across business teams and help drive toward a shared, canonical view.
Design and build the rules and validation logic that detects breaks (discrepancies between source data and ontology expectations), and continuously improve rule coverage and precision as new sources and use cases are onboarded.
Build and maintain reconciliation logic that compares source system data against the canonical ontology and surfaces exceptions in a way business users can triage.
Maintain clear documentation of ontology structures, mapping conventions, and rule logic so the model stays understandable as it grows.
Build and maintain ingestion connectors that bring new source systems onto the ontology platform, working within the frameworks and patterns established by the Engineering Lead.
Contribute to core platform services and APIs that support mapping, configuration, and rules management, so business users can define and adjust behavior without engineering intervention per source.
Leverage the foundational data platform's ingestion, storage, and compute capabilities rather than duplicating them; escalate capability gaps to the Engineering Lead rather than building bespoke infrastructure workarounds.
Adopt and champion an agentic SDLC approach, using AI coding agents and related tooling as a core part of your own development workflow to substantially multiply engineering throughput across connector development, rules/reconciliation logic, and pipeline maintenance, targeting materially higher output than traditional development approaches, not incremental gains.
Design and maintain the data models, schemas, and pipelines that store and serve ontology-mapped data for downstream consumption.
Build and optimize data transformation logic (ETL/ELT) that moves data from source systems through cleansing, harmonization, and mapping into its canonical ontology form.
Ensure data quality, lineage, and performance across the pipeline, from ingestion through to consumption, on top of the foundational platform's storage and compute layers.
Own the end-to-end onboarding process for new data sources and business use cases, from initial mapping through to production deployment.
Support production operations for the platform: monitoring, incident response, troubleshooting, and resolving data or pipeline issues.
Build and maintain runbooks, dashboards, and documentation that make the platform easier to operate and support over time.
Work directly with business users during onboarding to help them understand mapping results, breaks, and how to use the platform's configuration tools.