Data Engineer/Manager

Title: Data Engineer Architect/Manager Type: Full-time

Remote:
Job Description:
Seeking a highly Seasoned Data Engineer 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 Client 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 engineering operations and management strategy for AI/ML and Generative AI, specifically focusing on robust data testing and high-performance RAG (Retrieval-Augmented Generation) architectures.
Key Requirements & Qualifications
Core Expertise

• 15+ Years in Data Engineering: 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:
• Client 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).

Advanced Technical Skills:

• ML & LLM Data Engineering:
• 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 Engineering (R): Proficiency in R for advanced data profiling, statistical validation of data migrations, and building automated data quality frameworks to meet regulatory audit requirements.
• I don;t think we need to restrict this to R - just data profiling and statistical analysis Generative AI & Prompt Engineering: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.

Required Certifications:

• Certified Data Management Professional (CDMP) - Master level preferred.
• PostgreSQL Professional Certification (e.g., PostgreSQL Associate/Professional or EDB Certified Professional).
• 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).

Primary Responsibilities

• Data Strategy for AI: Architect the data layer for LLM-powered applications, ensuring that RAG systems have access to high-quality, governed, and real-time context from PostgreSQL.
• Advanced Data Testing & Observability: Design and implement a "Test-First" data engineering culture. Develop automated frameworks to validate data integrity, freshness, and distribution at every stage of the ELT/ML pipeline.
• Standardized Financial Modeling: Architect database schemas that natively support ISO 20022 structures, ensuring seamless interoperability for AI-driven financial analysis.
• Process Excellence (CMMI): Define and enforce data engineering workflows that meet CMM Level 3/4 standards, specifically applying these to the fast-moving AI/ML development lifecycle.
• Regulatory Data Governance: Ensure AI and ML data pipelines adhere to Client 239 and FFIEC AIO standards, focusing on model interpretability and data provenance.
• Data Mastering & Quality: Architect MDM solutions using R and SQL to build automated validation suites that identify anomalies before they reach downstream ML models or RAG systems.
• AI-Assisted Engineering: Capabilities Develop internal tools using Prompt Engineering to accelerate developer productivity while maintaining strict code governance and auditability.

Bonus Qualifications

• Experience with PG vector and optimizing high-dimensional vector similarity searches in Postgres.
• Contributions to PostgreSQL open-source projects or AI/ML data frameworks.
• 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.