Enterprise Data Services Manager

National Digital Trust Company (In Organization)•New York, NY

2d•Remote

About The Position

National Digital Trust Company is seeking a highly seasoned Data Architect/Manager with a minimum of 15 years of experience in banks or related financial services to support the architecture, development, and optimization of our data infrastructure within a highly regulated financial environment. The ideal candidate is a "SQL Master" with extensive experience in the SQL and PostgreSQL ecosystem, capable of designing high-performance database schemas while ensuring strict adherence to global banking and federal regulatory standards. In this role, you will act as the primary authority on data modeling, statistical data engineering, and data mastering. You will lead the implementation of data lineage and quality controls required for BCBS 239 compliance, ensure alignment with ISO 20022 messaging standards, and maintain process excellence following ISO 9000 and CMM/CMMI frameworks. Furthermore, you will spearhead the data operations and management strategy for AI/ML and Generative AI, specifically focusing on robust data testing and high-performance RAG (Retrieval-Augmented Generation) architectures.

Requirements

15+ Years in Data Management & Operations: A proven track record of building and maintaining enterprise-grade data pipelines and large-scale distributed systems, specifically within the Financial Services or Fintech sectors.
Regulatory & Financial Standards Expertise: BCBS 239: Deep understanding of the Basel Committee’s principles for effective risk data aggregation and risk reporting.
ISO 20022: Expert knowledge of the ISO 20022 universal financial industry message scheme.
FFIEC AIO: Practical experience aligning data architecture and infrastructure operations with the FFIEC booklet on Architecture, Infrastructure, and Operations.
Quality & Process Maturity: ISO 9000: Experience implementing and maintaining Data Quality Management Systems (DQMS).
CMM / CMMI: Experience operating within Level 3+ organizations where defined, standardized, and integrated processes are mandatory.
SQL Mastery: Expert-level SQL skills, including advanced window functions, recursive queries, CTEs, and complex joins. Ability to write highly efficient, readable, and maintainable code for high-concurrency environments.
PostgreSQL Specialization: At least 8-10 years of deep, hands-on experience specifically with PostgreSQL, including internal mechanisms (MVCC, WAL, VACUUM), partitioning, and advanced indexing (pgvector, GIN, GiST).
Data Integrity & State Validation: Able to design and run Row Verification: Querying the DB to ensure a new record exists with the exact values sent in the API payload. Data Truncation: Checking if long strings sent to the API were accidentally cut off by a database column limit. Default Values: Ensuring fields not sent in the API (like created_at or is_active) were populated correctly by DB triggers or application logic.
Data Structures: Proficiency in JSON and XML. You should know how to navigate nested objects and arrays to extract specific data. Hands-on experience with OAuth 2.0, JWT (JSON Web Tokens), API Keys, and Bearer Tokens. Using JavaScript to dynamically generate data (e.g., timestamps or hashed signatures) before a request is sent. Use natural language to generate test suites, debug failing requests, and even create documentation. Familiarity with REST, JSON, OpenAPI Spec, CI/CD integration.
Leveraging visual, low-code builder to map out complex logic, conditional branching (If/Else), and loops between multiple services. Using tools to generate client code in languages like Python, Java, or Node.js to jumpstart the actual implementation in the codebase or for testing. Using CSV or JSON files to test hundreds of scenarios (e.g., valid users, expired accounts, unauthorized countries) in a single click. Design and run automated tests every hour/day to ensure that a change in one microservice hasn't accidentally broken another, etc. etc.
Postman Library (External Modules): You can use tools like Postman-to-SQL or custom Node.js bridges to run SQL queries directly from your Postman "Tests" tab. You can create basic Comparison Logic: 1. Postman hits the API and saves the response as a variable. 2. A script triggers a DB query. 3. A test script compares the JSON response to the SQL Result Set.
ML & LLM Data: Preparation: Expert at designing data pipelines for machine learning (MLOps) and large language models (LLMOps). Experience with feature stores, data labeling workflows, and vector database integration.
RAG Architecture: Deep understanding of Retrieval-Augmented Generation (RAG) patterns. Proficiency in optimizing PostgreSQL (using pgvector) for semantic search, hybrid search (keyword + vector), and high-fidelity context retrieval.
Automated Data Testing: Mastery of data testing frameworks (e.g., Great Expectations, dbt-tests, or custom R-based suites). Experience implementing circuit breakers in pipelines, data contract testing, and regression testing for large-scale migrations.
Statistical (R): Proficiency in R or other modern languages for advanced data profiling, statistical validation of data migrations, and building automated data quality frameworks to meet regulatory audit requirements.
Generative AI & Prompt: Preparation: Advanced ability to design and refine prompts for LLMs to automate SQL generation, translate natural language to complex Postgres queries, and perform automated schema documentation.
Data Modeling & Mastering: Expert knowledge of OLTP vs. OLAP modeling and Data Vault 2.0. Proven experience in Data Mastering, including entity resolution and "Golden Record" management.
Experience with CDC (Change Data Capture) tools like Debezium, Kakfa for real-time RAG updates.
Familiarity with Infrastructure as Code (Terraform) for compliant, repeatable database and AI infrastructure provisioning.

Nice To Haves

Certified Data Management Professional (CDMP) - Master level preferred.
SQL Certification (PostgreSQL Professional Certification (e.g., PostgreSQL Associate/Professional or EDB Certified Professional) preferred/ideal)
Oracle Certified Professional: SQL Developer or equivalent high-level SQL mastery credential.
AWS/Google/Azure Professional Data Engineer or Machine Learning Engineer certifications.
Six Sigma or ISO 9001 Internal Auditor certification (Preferred).
CMMI Associate or similar process maturity credentials (Preferred).

Responsibilities

Design a data layer for LLM-powered applications, ensuring that RAG systems have access to high-quality, governed, and real-time context from PostgreSQL.
Design and implement a "Test-First" data culture.
Develop automated frameworks to validate data integrity, freshness, and distribution at every stage of the ELT/ML pipeline.
Define database schemas that natively support ISO 20022 structures, ensuring seamless interoperability for AI-driven financial analysis.
Define and enforce data management workflows that meet CMM Level 3/4 standards, specifically applying these to the fast-moving AI/ML development lifecycle.
Ensure AI and ML data pipelines adhere to BCBS 239 and FFIEC AIO standards, focusing on model interpretability and data provenance.
Design MDM solutions using R, SQL and other tools to build automated validation suites that identify anomalies before they reach downstream ML models or RAG systems.
Develop internal tools to accelerate developer productivity while maintaining strict data governance and auditability.
Explore and leverage pgvector and optimizing high-dimensional vector similarity searches in Postgres.
Contributions to PostgreSQL open-source projects or AI/ML data frameworks.