Senior Data Engineer / Architect

The Role

We are hiring a Senior Data Engineer / Architect to own the technical execution of our Golden Record platform and the broader data infrastructure that extends from it. The Golden Record is our central identity resolution system: it takes messy, overlapping client and instrument data from five source systems and resolves it into canonical entities that every downstream system can trust.

This is not a “build dashboards” role. You will design and build the core identity platform, the event bus that connects every system in the firm, and the analytical data warehouse that turns resolved identity into business insight. You'll work directly with the CTO (who owns the architecture) and a business stakeholder who owns the data quality and client relationships. Areas of ownership include:

Identity Resolution Platform

The Golden Record — our central system for answering “who is this client?” and “what is this instrument?” across the firm. You'll own the entity registry, the resolver API (name in, canonical ID out), and a human-in-the-loop review queue where the system proposes matches and a business user confirms. Includes integration with external reference data sources (OpenFIGI, EDGAR, GLEIF/LEI) for automated enrichment.

Event Bus + Integration Architecture

AWS EventBridge as the firm's enterprise event bus. All source system data flows through the Golden Record for identity resolution before events are published — every event on the bus carries resolved canonical IDs. You'll own the event contracts (JSON Schema with generated TypeScript/Python types), the schema enforcement, and the expansion from identity events into trade, research, CRM, and readership domains.

Data Warehouse + Analytics

The analytical layer built on dbt — staging models, dimensional marts, and bridge tables that join identity with operational data. BI dashboards for client coverage, trading activity, readership analytics, and CRM gap analysis. Data quality monitoring to track resolution rates, alias coverage, and entity drift.

Must Have Requirements

• 8+ years building data platforms, backend services, or data engineering infrastructure
• Deep experience with AWS serverless (Lambda, API Gateway, EventBridge, Aurora, CDK)
• Strong PostgreSQL skills — schema design, query optimization, migrations
• Production experience with event-driven architecture (EventBridge, SNS/SQS, Kafka, or similar)
• Hands-on data warehouse and ETL/ELT experience — you've designed schemas, built pipelines, and operated a warehouse in production, not just queried one
• dbt proficiency — staging/mart patterns, incremental models, testing, documentation
• TypeScript or Python fluency (ideally both; TypeScript is primary for infrastructure)
• Experience with entity resolution, master data management, or identity matching problems
• Active use of AI-assisted development tools (Cursor, Claude Code, Copilot, or similar) — we build with AI, not around it
• Comfort working autonomously in a small team with direct access to business stakeholders

Nice to Have

• Familiarity with broker-dealer operations, trade lifecycle, or research distribution
• Experience with CRM systems (Tier1/S&P Global, Salesforce, or similar)
• Data quality frameworks and monitoring (Great Expectations, dbt tests, custom)
• Prior experience at a small firm where you owned the full stack, not just one layer

What this role is not

To set expectations clearly:
• This is not a data science or ML role. The entity resolution logic is rule-based and human-in-the-loop, not probabilistic matching. AI is part of how we build, not the product itself.
• This is not a front-end role. The primary UI is BI dashboards and a lightweight HITL review queue, not a custom web application.
• This is not a large-team management role. You'll be the primary technical executor, working with 1–2 other developers and a business stakeholder. Leadership here means technical ownership, not people management.
• This is not a “move fast and break things” environment. We're a regulated broker-dealer. Data accuracy matters more than speed. The architecture is thoughtful and intentional.

How we work

• Small team, high trust. You'll work directly with the CTO and a business owner who knows every client by name. Decisions happen in conversation, not in tickets.

• Architecture-first. The system design is well-defined (event-driven, CDK, three-repo strategy). You're executing a clear vision, not inventing the architecture from scratch — but you'll have significant input on implementation details and we welcome challenges to the design.

• AI-native development. We use LLMs and agentic tools throughout our engineering workflow — for code generation, architecture exploration, documentation, and data analysis. If you're not already working this way, this isn't the right fit.

• Human-in-the-loop is core. We don't trust fully automated entity resolution for a firm this size. The system proposes, the business user decides, the system learns. You need to be comfortable building systems where humans are in the critical path.

• Legacy respect. We're not ripping out old systems. We're wrapping them with a clean architecture (strangler fig pattern) and letting the Golden Record become the authoritative source over time.