Chief Architect

TEKsystems•New York, NY

3d•$225,000 - $300,000•Remote

About The Position

We are seeking an Enterprise Architect that can take our existing platform and modernize it to meet the current and future demands of the Secondary Mortgage market. This person will be hands on technical but also leading small team of developers and QA.

Requirements

7+ years of mortgage experience with specialty of the Secondary Mortgage market which where mortgages sold and packaged for investment vehicles.
10+ years of software engineering and architecture experience, with at least 5 years leading enterprise-scale platform design and delivery.
Demonstrated track record of building and shipping commercial enterprise software platforms in financial services or B2B SaaS — not advisory or architecture-only roles. You have built things that work in production.
Deep proficiency with relational database architecture: schema design, migration strategy, data governance, and provenance modeling. Postgres experience strongly preferred.
Experience deploying rules engines and BPM platforms in production environments (Camunda, Drools, or equivalent). Must understand the rules/workflow separation principle at an architectural level.
Hands-on experience with ETL pipeline design, integration architecture, and API governance in environments with multiple third-party data sources.
Demonstrated experience leading AI-assisted development programs: prompt engineering governance, LLM integration into engineering workflows, and AI-generated artifact quality control.
Proficiency with .NET Core, C#, React, and AWS cloud-native services (ECS, RDS, Redshift, Lambda, API Gateway).
Experience with CI/CD automation, automated testing frameworks, and build process ownership — including measurable quality metrics.
This is a start up environment so this resource must be a "go getter" and someone who is proactive vs. reactive. Imperative to have solid communications skills and be able to set proper expectations.

Nice To Haves

AWS Certified Solutions Architect – Professional or equivalent preferred.

Responsibilities

Own and enforce the five-layer architectural blueprint across all systems, data, and integration layers. The canonical data model is non-negotiable infrastructure. Establish and maintain schema governance — no uncontrolled field creation, no custom code bypassing platform tools. Every field has a defined source tier, business definition, and provenance record.
Complete and govern the DynamoDB-to-Postgres migration, including schema design, data validation, and deprecation sequencing for legacy DynamoDB tables.
Own the Silver data dictionary architecture: field definitions, source lineage from stated data through manufactured data to authoritative Silver designation, tier classification (Tier 1 direct institutional pull, Tier 2 third-party, Tier 3 document submission), and target/consumer documentation.
Establish and chair the Architecture Review Council. All significant design decisions are reviewed, documented, and held to pattern standards before implementation.
Define and enforce the Anti-Rube Goldberg principle: one governed way to solve each class of problem. Reuse over novelty. No proliferation of bespoke solutions.
Deploy and govern the componentized rules engine and BPM layer that is the operational core of the certified loan process across all three seller channels: Flow/Delegated, Non-Delegated/Mini-Corr, and Bulk/Institutional. This layer is what makes the 600-to-6 complexity abstraction possible at scale.
Complete the Camunda (or equivalent) rules engine evaluation, make the selection decision, and lead deployment. The rules engine must be business-authorable, engineer-reviewed, and vendor-extractable.
Enforce the workflow/rules separation principle in all builds: workflow orchestrates, rules validate. Logic never embeds in workflow steps.
Build the componentized rules library in collaboration with the credit risk and underwriting leadership, supporting channel-specific configurations without duplicating core logic.
Design the rules engine to support the eight certified loan process gate validations across delegated, non-delegated, and bulk channels, with third-party data integration at each gate where applicable.
Establish and govern the AI execution layer as the fifth layer of the platform architecture. AI generates ETL mappings, rules components, test cases, and workflow artifacts — but only within governed guardrails. The business owns outcomes. Engineering owns the platform. The Enterprise Architect owns the governance model that makes this safe.
Establish the Center of Excellence (CoE) for AI-assisted development: prompt libraries, pattern standards, testing/retraining protocols, and senior review gates for all AI-generated artifacts before production deployment.
Own prompt engineering governance centrally. One owner, one library, one set of standards. No proliferation of individual prompt sets outside the governed framework.
Lead adoption of AWS Q for Developer, GitHub Copilot, and Anthropic Claude in engineering workflows, with measurable targets for code generation, test automation, and documentation quality.
Build and govern the LLM-assisted rules generation capability: ingesting underwriting guidelines (including the Fannie Mae Seller Guide and investor-specific overlays), generating componentized rules, and validating outputs against the canonical data model.
Design the AI co-pilot model for the non-delegated manufacturing process: reviewing loan files during origination, identifying exceptions, and preparing clean handoffs to buyers — eliminating post-closing defect discovery.
Own the integration architecture that connects our canonical data model to the third-party data sources that validate and elevate stated data across the loan manufacturing lifecycle. This is the provenance engine that makes the Silver data designation credible.
Lead ETL platform selection and deployment (Adeptia, Boomi, or equivalent): normalized inbound data, validated structure, canonical model mapping, and auditable pipeline governance.
Architect and govern integrations with Tier 1 data sources: credit bureaus (Equifax, Experian, TransUnion), income verification (The Work Number, Experian Verify, Finicity), tax transcripts (4506-C via Experian or CoreLogic), and property/title data (First American, Stewart Title, CoreLogic AVM).
Design the extensible API and integration layer for LOS/POS connectivity, Optimal Blue PPE integration, Tradeweb API/widget build, and future Loan Passport and Canton Network digital asset infrastructure.
Govern the MP1/MP2 security architecture resolution: scope the decision, evaluate options, and drive to closure. This is a near-term gating item with approximately $150K cost implication.
The tokenization lane is an independent strategic workstream, not a long-range aspiration. The physical note immobilization architecture (single custodian, MERS registration, servicer named at closing) is the Layer 1 and Layer 2 infrastructure. The Enterprise Architect designs the technical layer on top of it.
Architect the digital certificate framework under UCC Article 8: instrument design, registry architecture, transfer and verification protocols, and custodial integration with ComputerShare as primary custodian.
Design the Loan Passport MVP: the loan-level digital identity record that travels with the collateral through the trading lifecycle, tamper-sealed at closing, supporting Canton Network distribution via Tradeweb.
Evaluate blockchain frameworks (Hyperledger, Canton Network, or equivalent private network architecture) for smart contract deployment supporting servicing rights transfer and collateral control — in partnership with legal and capital markets leadership.
Ensure all digital asset designs maintain SOC 2 compliance, data privacy requirements, and regulatory alignment. This lane runs in parallel with the core platform build under its own governance.
The Enterprise Architect establishes the disciplined, repeatable build process that has not had at this level of rigor. Quality is designed in, not inspected in.
Own end-to-end build and release process: build health metrics, automated testing standards, release readiness gates, and post-mortem analysis loops.
Mentor senior engineers on architecture principles, design patterns, and AI-assisted development practices. Chair design reviews and enforce cross-team adherence to standards.
Partner with HR and leadership on talent assessment and development for key technical roles. Be honest about gaps in the current team and constructive about how to address them.
Ensure every AI-generated artifact — ETL mapping, rules component, workflow step, test case — passes pattern check, test harness, and security review before production deployment.