Senior Data Platform Engineer

SS&C Technologies•Washington, NV

About The Position

We are seeking a Senior Data Platform Engineer to help build, scale, and operate our bespoke enterprise data platform, the foundational infrastructure layer that ingestion, ontology, and business application teams build on top of. This is a hands-on, individual-contributor role for someone who wants deep technical ownership of platform internals rather than owning specific business data pipelines: our platform stack spans Airflow, Iceberg, Spark, Flink, Kafka, Nessie, Trino, and StarRocks, along with supporting UI components for platform users. You will not own the business logic of individual data pipelines; those are owned by application and business teams. Instead, you will build and harden the core platform capabilities that make those pipelines possible: ingestion frameworks, data cleansing/harmonization utilities, storage and compute infrastructure, and the APIs and agentic (MCP) interfaces that let both humans and AI agents access and operate on platform data and capabilities. You'll also be expected to bring an agentic software development approach to your own work, using AI coding agents and tooling to meaningfully multiply your engineering throughput, not just as an experiment on the side.

Requirements

8-12 years of software/data engineering experience, with significant hands-on experience building or operating large-scale data platform infrastructure (not primarily building individual application-level data pipelines).
Deep, hands-on experience with at least several of: Apache Airflow, Apache Iceberg, Apache Spark, Apache Flink, Apache Kafka, Nessie, Trino, StarRocks, with the ability to reason confidently about the rest and ramp up quickly where needed.
Strong understanding of lakehouse architecture: table formats, catalog management, query engines, and the trade-offs between batch and streaming processing.
Experience designing and building APIs for platform capabilities, with an understanding of API governance, versioning, and security for multi-tenant platform consumers.
Direct experience with, or strong interest and rapid aptitude for, building MCP servers or similar agent-facing interfaces that expose data/capabilities to AI agents and LLM-based tooling.
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.
Strong systems engineering fundamentals: distributed systems, performance tuning, capacity planning, and building for fault tolerance and resilience at scale.
Experience building and maintaining production observability (metrics, logging, tracing) for complex, multi-service data infrastructure.
Comfortable operating as an individual contributor with deep technical ownership, while also being able to mentor others and influence engineering practice without formal authority.

Nice To Haves

Experience building internal developer platforms or "paved road" tooling that other engineering teams adopt as self-service infrastructure.
Experience building agent-facing tooling or infrastructure beyond MCP (e.g., tool-use frameworks, agent orchestration, guardrails for autonomous agent actions).
Familiarity with UI/front-end development sufficient to build or contribute to platform-facing dashboards and tooling (not a primary requirement, but a plus).
Experience in financial services or another regulated, data-intensive industry, given the auditability and data-quality expectations typical of the domain.
Contributions to open-source projects in the Airflow/Iceberg/Spark/Flink/Trino ecosystem.

Responsibilities

Design, build, and operate core platform capabilities on top of Airflow, Spark, and Flink for batch and streaming data processing, providing reusable frameworks that application/business teams use to build their own pipelines rather than building pipelines on their behalf.
Own the data lakehouse layer built on Iceberg, Nessie (catalog/versioning), Trino, and StarRocks, including table format management, catalog governance, query performance, and storage lifecycle (compaction, partitioning, retention).
Build and maintain Kafka-based streaming infrastructure (topics, schema management, connectors) that supports both platform-internal needs and consumption by downstream teams.
Develop reusable ingestion, cleansing, and harmonization frameworks/libraries that application teams can adopt to bring data onto the platform in a consistent, quality-controlled way, without the platform team owning each pipeline's business logic.
Build and maintain UI components that give platform users (engineers, analysts, business teams) visibility into platform state, data catalogs, job status, and data quality.
Design and build APIs that expose platform data and capabilities (ingestion, querying, catalog metadata, job orchestration) to downstream teams and services in a well-governed, secure, and performant way.
Build and maintain MCP (Model Context Protocol) servers/interfaces that expose platform data, catalogs, and lineage to AI agents and LLM-based tools, enabling agentic consumption of platform capabilities.
Extend agentic enablement beyond data access by building the tooling and guardrails that allow AI agents to safely operate parts of the platform itself (e.g., triggering pipeline runs, querying job status, remediation actions), with appropriate human-in-the-loop controls where needed.
Partner with platform consumers (including the Ontology Platform team) to understand their API/agentic access needs and translate them into platform capability requirements.
Ensure the platform scales reliably as data volume, source count, and consumer count grow, through capacity planning, performance tuning, and architectural evolution across the stack.
Design for resilience: fault tolerance, disaster recovery, data consistency guarantees, and graceful degradation across ingestion, storage, and compute layers.
Build and maintain observability (metrics, logging, tracing, alerting) across the platform stack to detect and diagnose issues before they impact downstream teams.
Drive on-call/production support practices for the platform, including runbooks and incident response processes.
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 (targeting materially higher output than traditional development approaches, not incremental gains).
Establish and evolve engineering best practices for the platform team: testing strategy, CI/CD, infrastructure-as-code, and code/architecture review standards suited to a bespoke, multi-technology data platform.
Mentor other engineers on platform internals and on effective use of agentic development tooling, even without formal management responsibility.